def fit(self,
frequency,
recency,
T,
iterative_fitting=1,
initial_params=None,
verbose=False):
"""
This methods fits the data to the MBG/NBD model.
Parameters:
frequency: the frequency vector of customers' purchases (denoted x in literature).
recency: the recency vector of customers' purchases (denoted t_x in literature).
T: the vector of customers' age (time since first purchase)
iterative_fitting: perform `iterative_fitting` additional fits to find the best
parameters for the model. Setting to 0 will improve performances but possibly
hurt estimates.
initial_params: set the initial parameters for the fitter.
verbose: set to true to print out convergence diagnostics.
Returns:
self, with additional properties and methods like params_ and predict
"""
super(self.__class__, self).fit(
frequency, recency, T, iterative_fitting, initial_params, verbose
) #although the partent method is called, this class's _negative_log_likelihood is referenced
self.generate_new_data = lambda size=1: modified_beta_geometric_nbd_model(
T, *self._unload_params('r', 'alpha', 'a', 'b'), size=size
) #this needs to be reassigned from the parent method
return self
def simulate(pareto, mbg):
times = [10, 365, 3650, 36500]
r_par, alpha_par, s, beta = pareto.params_
r_mbg, alpha_mbg, a, b = mbg.summary['coef']
size = 100
for duration in times:
generated_customers_par = pareto_nbd_model(T=duration,
r=r_par,
alpha=alpha_par,
s=s,
beta=beta,
size=size)
generated_customers_mbg = modified_beta_geometric_nbd_model(
T=duration, r=r_mbg, alpha=alpha_mbg, a=a, b=b, size=size)
print("Duration: {}".format(duration))
print("Pareto/NBD : Modified BG/NBD")
print("Number of Purchases: {} : Number of Purchases: {}".format(
generated_customers_par['frequency'].sum(),
generated_customers_mbg['frequency'].sum()))
print("Number of Customers Alive: {} : Number of Customers Alive: {}".
format(generated_customers_par['alive'].sum(),
generated_customers_mbg['alive'].sum()))
print(
"-----------------------------------------------------------------"
)
def fit(
self, frequency, recency, T, weights=None, initial_params=None, verbose=False, tol=1e-7, index=None, **kwargs
):
"""
Fit the data to the MBG/NBD model.
Parameters
----------
frequency: array_like
the frequency vector of customers' purchases
(denoted x in literature).
recency: array_like
the recency vector of customers' purchases
(denoted t_x in literature).
T: array_like
customers' age (time units since first purchase)
weights: None or array_like
Number of customers with given frequency/recency/T,
defaults to 1 if not specified. Fader and
Hardie condense the individual RFM matrix into all
observed combinations of frequency/recency/T. This
parameter represents the count of customers with a given
purchase pattern. Instead of calculating individual
log-likelihood, the log-likelihood is calculated for each
pattern and multiplied by the number of customers with
that pattern.
verbose : bool, optional
set to true to print out convergence diagnostics.
tol : float, optional
tolerance for termination of the function minimization process.
index: array_like, optional
index for resulted DataFrame which is accessible via self.data
kwargs:
key word arguments to pass to the scipy.optimize.minimize
function as options dict
Returns
-------
ModifiedBetaGeoFitter:
With additional properties and methods like ``params_`` and ``predict``
"""
# although the parent method is called, this class's
# _negative_log_likelihood is referenced
super(ModifiedBetaGeoFitter, self).fit(
frequency, recency, T, weights, initial_params, verbose, tol, index=index, **kwargs
)
# this needs to be reassigned from the parent method
self.generate_new_data = lambda size=1: modified_beta_geometric_nbd_model(
T, *self._unload_params("r", "alpha", "a", "b"), size=size
)
self.variance_matrix_ = self._compute_variance_matrix()
self.standard_errors_ = self._compute_standard_errors()
self.confidence_intervals_ = self._compute_confidence_intervals()
return self
def fit(self,
frequency,
recency,
T,
iterative_fitting=1,
initial_params=None,
verbose=False,
tol=1e-4,
index=None):
"""
This methods fits the data to the MBG/NBD model.
Parameters:
frequency: the frequency vector of customers' purchases (denoted x in literature).
recency: the recency vector of customers' purchases (denoted t_x in literature).
T: the vector of customers' age (time since first purchase)
iterative_fitting: perform iterative_fitting fits over random/warm-started initial params
initial_params: set the initial parameters for the fitter.
verbose: set to true to print out convergence diagnostics.
index: index for resulted DataFrame which is accessible via self.data
Returns:
self, with additional properties and methods like params_ and predict
"""
super(self.__class__, self).fit(
frequency,
recency,
T,
iterative_fitting,
initial_params,
verbose,
tol,
index=index
) # although the parent method is called, this class's _negative_log_likelihood is referenced
self.generate_new_data = lambda size=1: modified_beta_geometric_nbd_model(
T, *self._unload_params('r', 'alpha', 'a', 'b'), size=size
) # this needs to be reassigned from the parent method
return self
