Modelo BG/NBD con covariables: Problemas con la recuperación de los parámetros a, b#
Importaciones#
import arviz as az
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import xarray as xr
from fastprogress.fastprogress import progress_bar
from pymc_marketing import clv
from pymc_extras.prior import Prior
from tests.conftest import set_model_fit
# Plotting configuration
az.style.use("arviz-darkgrid")
plt.rcParams["figure.figsize"] = [12, 7]
plt.rcParams["figure.dpi"] = 100
plt.rcParams["figure.facecolor"] = "white"
%load_ext autoreload
%autoreload 2
%config InlineBackend.figure_format = "retina"
The autoreload extension is already loaded. To reload it, use:
%reload_ext autoreload
Replicar la prueba#
Leer los datos#
data = pd.read_csv("data/clv_quickstart.csv")
data["customer_id"] = data.index
data.head()
| frequency | recency | T | monetary_value | customer_id | |
|---|---|---|---|---|---|
| 0 | 2 | 30.43 | 38.86 | 22.35 | 0 |
| 1 | 1 | 1.71 | 38.86 | 11.77 | 1 |
| 2 | 0 | 0.00 | 38.86 | 0.00 | 2 |
| 3 | 0 | 0.00 | 38.86 | 0.00 | 3 |
| 4 | 0 | 0.00 | 38.86 | 0.00 | 4 |
Establecer covariables artificiales#
seed = sum(map(ord, "bgnbd-covar"))
rng = np.random.default_rng(seed)
N = data.shape[0]
data["purchase_cov1"] = rng.normal(size=N) / 2
data["purchase_cov2"] = rng.normal(size=N) / 4
data["dropout_cov"] = rng.normal(size=N) / 6
data.head()
| frequency | recency | T | monetary_value | customer_id | purchase_cov1 | purchase_cov2 | dropout_cov | |
|---|---|---|---|---|---|---|---|---|
| 0 | 2 | 30.43 | 38.86 | 22.35 | 0 | 0.999919 | -0.298447 | -0.058698 |
| 1 | 1 | 1.71 | 38.86 | 11.77 | 1 | -0.161161 | -0.083486 | 0.138539 |
| 2 | 0 | 0.00 | 38.86 | 0.00 | 2 | -0.060377 | 0.444374 | -0.237762 |
| 3 | 0 | 0.00 | 38.86 | 0.00 | 3 | 1.100146 | -0.230825 | -0.055118 |
| 4 | 0 | 0.00 | 38.86 | 0.00 | 4 | -0.841947 | -0.261404 | 0.085952 |
Establecer la configuración#
purchase_covariate_cols = ["purchase_cov1", "purchase_cov2"]
dropout_covariate_cols = ["dropout_cov"]
non_nested_priors = dict(
a_prior=Prior("Uniform", lower=0, upper=1),
b_prior=Prior("Uniform", lower=0, upper=1),
)
covariate_config = dict(
purchase_covariate_cols=purchase_covariate_cols,
dropout_covariate_cols=dropout_covariate_cols,
)
model_with_covariates = clv.BetaGeoModel(
data,
model_config={**non_nested_priors, **covariate_config},
)
Ajuste simulado#
true_params = dict(
a_scale=1,
b_scale=1,
alpha_scale=1,
r=1,
purchase_coefficient_alpha=np.array([1.0, -2.0]),
dropout_coefficient_a=np.array([3.0]),
dropout_coefficient_b=np.array([3.0]),
)
# Mock an idata object for tests requiring a fitted model
chains = 2
draws = 200
n_purchase_covariates = len(purchase_covariate_cols)
n_dropout_covariates = len(dropout_covariate_cols)
mock_fit_dict = {
"r": rng.normal(true_params["r"], 1e-3, size=(chains, draws)),
"alpha_scale": rng.normal(
true_params["alpha_scale"], 1e-3, size=(chains, draws)
),
"a_scale": rng.normal(
true_params["a_scale"], 1e-3, size=(chains, draws)
),
"b_scale": rng.normal(
true_params["b_scale"], 1e-3, size=(chains, draws)
),
"purchase_coefficient_alpha": rng.normal(
true_params["purchase_coefficient_alpha"],
1e-3,
size=(chains, draws, n_purchase_covariates),
),
"dropout_coefficient_a": rng.normal(
true_params["dropout_coefficient_a"],
1e-3,
size=(chains, draws, n_dropout_covariates),
),
"dropout_coefficient_b": rng.normal(
true_params["dropout_coefficient_b"],
1e-3,
size=(chains, draws, n_dropout_covariates),
),
}
mock_fit_with_covariates = az.from_dict(
mock_fit_dict,
dims={
"purchase_coefficient_alpha": ["purchase_covariate"],
"dropout_coefficient_a": ["dropout_covariate"],
"dropout_coefficient_b": ["dropout_covariate"],
},
coords={
"purchase_covariate": purchase_covariate_cols,
"dropout_covariate": dropout_covariate_cols,
},
)
set_model_fit(model_with_covariates, mock_fit_with_covariates)
Establecer datos de prueba#
reps = 30
test_data_zero = pd.DataFrame(
{
"customer_id": range(3 * reps),
"frequency": [1, 2, 0] * reps,
"recency": [7, 5, 2] * reps,
"purchase_cov1": [0, 0, 0] * reps,
"purchase_cov2": [0, 0, 0] * reps,
"dropout_cov": [0, 0, 0] * reps,
"T": [20, 20, 20] * reps,
"future_t": [2, 3, 4] * reps,
"n_purchases": [2, 1, 4] * reps,
}
)
Distribuciones de muestreo#
Muestra sin covariables#
res_zero = model_with_covariates.distribution_new_customer(test_data_zero).mean(
("chain", "draw")
)
Sampling: [dropout, purchase_rate, recency_frequency]
sns.pairplot(res_zero.to_dataframe().reset_index().drop(columns=["customer_id"]), hue="obs_var")
/opt/homebrew/envs/pymc-marketing-dev/lib/python3.10/site-packages/seaborn/axisgrid.py:123: UserWarning: The figure layout has changed to tight
self._figure.tight_layout(*args, **kwargs)
<seaborn.axisgrid.PairGrid at 0x349d47250>
Muestra con covariables#
test_data_alt = test_data_zero.assign(
purchase_cov=1, # positive coefficient
purchase_cov2=-1, # negative coefficient
dropout_cov=1, # positive coefficient
)
res_high = model_with_covariates.distribution_new_customer(test_data_alt).mean(
("chain", "draw")
)
Sampling: [dropout, purchase_rate, recency_frequency]
sns.pairplot(res_high.to_dataframe().reset_index().drop(columns=["customer_id"]), hue="obs_var")
/opt/homebrew/envs/pymc-marketing-dev/lib/python3.10/site-packages/seaborn/axisgrid.py:123: UserWarning: The figure layout has changed to tight
self._figure.tight_layout(*args, **kwargs)
<seaborn.axisgrid.PairGrid at 0x3395b4910>
Combina ambos y examina las distribuciones.#
combined = pd.concat([res_zero.to_dataframe(), res_high.to_dataframe()], keys=["zero", "high"], names=["fit_type"])
sns.displot(combined, x="purchase_rate", hue="fit_type", binwidth=0.1).set(title="Purchase Rate")
/opt/homebrew/envs/pymc-marketing-dev/lib/python3.10/site-packages/seaborn/axisgrid.py:123: UserWarning: The figure layout has changed to tight
self._figure.tight_layout(*args, **kwargs)
<seaborn.axisgrid.FacetGrid at 0x324e116f0>
# Assertion
(res_zero["purchase_rate"] < res_high["purchase_rate"]).all()
<xarray.DataArray 'purchase_rate' ()> Size: 1B array(True)
sns.displot(combined, x="dropout", hue="fit_type").set(title="Dropout")
/opt/homebrew/envs/pymc-marketing-dev/lib/python3.10/site-packages/seaborn/axisgrid.py:123: UserWarning: The figure layout has changed to tight
self._figure.tight_layout(*args, **kwargs)
<seaborn.axisgrid.FacetGrid at 0x323efe890>
La deserción es la afirmación problemática.#
La introducción de covariables no está cambiando el valor de la probabilidad de abandono. Investigaremos esto a continuación.
# Assertion
(res_zero["dropout"] < res_high["dropout"])
<xarray.DataArray 'dropout' (customer_id: 90)> Size: 90B
array([False, True, True, True, True, True, False, True, True,
False, True, False, False, False, False, False, True, False,
False, True, False, True, False, False, False, True, False,
True, False, False, True, False, False, False, False, False,
False, False, True, False, False, False, True, False, False,
False, True, True, True, False, False, True, True, False,
True, False, False, False, False, True, False, True, True,
False, True, False, False, True, False, True, False, False,
True, True, True, True, False, False, False, False, False,
True, True, False, True, True, True, True, False, True])
Coordinates:
* customer_id (customer_id) int64 720B 0 1 2 3 4 5 6 ... 83 84 85 86 87 88 89sns.displot(combined, x="recency_frequency", hue="fit_type", col="obs_var", binwidth=0.05) #.set(title="Recency Frequency")
/opt/homebrew/envs/pymc-marketing-dev/lib/python3.10/site-packages/seaborn/axisgrid.py:123: UserWarning: The figure layout has changed to tight
self._figure.tight_layout(*args, **kwargs)
<seaborn.axisgrid.FacetGrid at 0x33a268460>
assert (
res_zero["recency_frequency"].sel(obs_var="frequency")
> res_high["recency_frequency"].sel(obs_var="frequency")
).all()
assert (
res_zero["recency_frequency"].sel(obs_var="recency")
> res_high["recency_frequency"].sel(obs_var="recency")
).all()
La distribución Beta y la covariable de abandono#
import scipy.stats as st
alpha (a) = beta (b)#
x = np.linspace(0, 1, 200)
alphas = [.5, 1., 2., 3., 4.]
betas = [.5, 1., 2., 3., 4.]
for a, b in zip(alphas, betas):
pdf = st.beta.pdf(x, a, b)
plt.plot(x, pdf, label=r'$\alpha$ = {}, $\beta$ = {}'.format(a, b))
plt.xlabel('x', fontsize=12)
plt.ylabel('f(x)', fontsize=12)
plt.legend(loc=1)
<matplotlib.legend.Legend at 0x34d469150>
Siempre que a = b, la distribución Beta es simétrica con E[X] = 0.5.
Esto tiene implicaciones sobre los valores esperados de nuestra variable «dropout». Introducir covariables solo estrecha nuestra distribución.
alpha (a) > beta (b)#
x = np.linspace(0, 1, 200)
alphas = np.array([.5, 1., 2., 3., 4.])
betas = 0.5*alphas
for a, b in zip(alphas, betas):
pdf = st.beta.pdf(x, a, b)
plt.plot(x, pdf, label=r'$\alpha$ = {}, $\beta$ = {}'.format(a, b))
Tenemos E[X] > 0.5
x = np.linspace(0, 1, 200)
alphas = np.array([.5, 1., 2., 3., 4.])
betas = 1.5*alphas
for a, b in zip(alphas, betas):
pdf = st.beta.pdf(x, a, b)
plt.plot(x, pdf, label=r'$\alpha$ = {}, $\beta$ = {}'.format(a, b))
plt.xlabel('x', fontsize=12)
plt.ylabel('f(x)', fontsize=12)
plt.legend(loc=1)
<matplotlib.legend.Legend at 0x34d49c490>
Tenemos E[X] < 0.5
Contraste con ParetoNBD#
El ParetoNBD utilizó Gamma para el proceso de abandono,
x = np.linspace(0, 1, 200)
alphas = np.array([.5, 1., 2., 3., 4.])
betas = 2*np.ones_like(alphas)
for a, b in zip(alphas, betas):
pdf = st.beta.pdf(x, a, b)
plt.plot(x, pdf, label=r'$\alpha$ = {}, $\beta$ = {}'.format(a, b))
plt.xlabel('x', fontsize=12)
plt.ylabel('f(x)', fontsize=12)
plt.legend(loc=1)
Aquí tenemos E[X] que aumenta monótonamente. La presencia de covariables incrementa la probabilidad de abandono.
Deberíamos cambiar nuestra afirmación a:#
res_zero["dropout"].std("customer_id") > res_high["dropout"].std("customer_id")
<xarray.DataArray 'dropout' ()> Size: 1B array(True)
%reload_ext watermark
%watermark -n -u -v -iv -w -p pymc,pytensor
Last updated: Sun Feb 02 2025
Python implementation: CPython
Python version : 3.10.16
IPython version : 8.31.0
pymc : 5.20.0
pytensor: 2.26.4
xarray : 2025.1.1
pandas : 2.2.3
pymc_marketing: 0.11.0
pymc : 5.20.0
fastprogress : 1.0.3
numpy : 1.26.4
arviz : 0.20.0
scipy : 1.12.0
seaborn : 0.13.2
matplotlib : 3.10.0
Watermark: 2.5.0