def test_beta_geometric_nbd_model_transactional_data(T, r, alpha, a, b,
observation_period_end,
freq, size):
np.random.seed(188898)
transaction_data = beta_geometric_nbd_model_transactional_data(
T=T,
r=r,
alpha=alpha,
a=a,
b=b,
observation_period_end=observation_period_end,
freq=freq,
size=size)
actual = summary_data_from_transaction_data(
transactions=transaction_data,
customer_id_col="customer_id",
datetime_col="date",
observation_period_end=observation_period_end,
freq=freq,
)
np.random.seed(188898)
expected = beta_geometric_nbd_model(T=T,
r=r,
alpha=alpha,
a=a,
b=b,
size=size)[[
"frequency", "recency", "T"
]]
expected["recency"] = expected["recency"].apply(np.ceil)
expected = expected.reset_index(drop=True)
actual = actual.reset_index(drop=True)
assert expected.equals(actual)
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 BG/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
"""
frequency = asarray(frequency)
recency = asarray(recency)
T = asarray(T)
_check_inputs(frequency, recency, T)
self._scale = _scale_time(T)
scaled_recency = recency * self._scale
scaled_T = T * self._scale
params, self._negative_log_likelihood_ = _fit(
self._negative_log_likelihood,
[frequency, scaled_recency, scaled_T, self.penalizer_coef],
iterative_fitting, initial_params, 4, verbose, tol)
self.params_ = OrderedDict(zip(['r', 'alpha', 'a', 'b'], params))
self.params_['alpha'] /= self._scale
self.data = DataFrame(vconcat[frequency, recency, T],
columns=['frequency', 'recency', 'T'])
if index is not None:
self.data.index = index
self.generate_new_data = lambda size=1: beta_geometric_nbd_model(
T, *self._unload_params('r', 'alpha', 'a', 'b'), size=size)
self.predict = self.conditional_expected_number_of_purchases_up_to_time
return self
def fit(self, frequency, recency, T, iterative_fitting=1, initial_params=None, verbose=False):
"""
This methods fits the data to the BG/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 peformance 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
"""
frequency = asarray(frequency)
recency = asarray(recency)
T = asarray(T)
_check_inputs(frequency, recency, T)
self._scale = _scale_time(T)
scaled_recency = recency * self._scale
scaled_T = T * self._scale
params, self._negative_log_likelihood_ = _fit(
self._negative_log_likelihood,
frequency,
scaled_recency,
scaled_T,
iterative_fitting,
self.penalizer_coef,
initial_params,
verbose,
)
self.params_ = OrderedDict(zip(["r", "alpha", "a", "b"], params))
self.params_["alpha"] /= self._scale
self.data = DataFrame(vconcat[frequency, recency, T], columns=["frequency", "recency", "T"])
self.generate_new_data = lambda size=1: beta_geometric_nbd_model(
T, *self._unload_params("r", "alpha", "a", "b"), size=size
)
self.predict = self.conditional_expected_number_of_purchases_up_to_time
return self
def fit(self,
frequency,
recency,
T,
iterative_fitting=1,
initial_params=None,
verbose=False):
"""
This methods fits the data to the BG/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 peformance 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
"""
frequency = asarray(frequency)
recency = asarray(recency)
T = asarray(T)
_check_inputs(frequency, recency, T)
self._scale = _scale_time(T)
scaled_recency = recency * self._scale
scaled_T = T * self._scale
params, self._negative_log_likelihood_ = _fit(
self._negative_log_likelihood, frequency, scaled_recency, scaled_T,
iterative_fitting, self.penalizer_coef, initial_params, verbose)
self.params_ = OrderedDict(zip(['r', 'alpha', 'a', 'b'], params))
self.params_['alpha'] /= self._scale
self.data = DataFrame(vconcat[frequency, recency, T],
columns=['frequency', 'recency', 'T'])
self.generate_new_data = lambda size=1: beta_geometric_nbd_model(
T, *self._unload_params('r', 'alpha', 'a', 'b'), size=size)
self.predict = self.conditional_expected_number_of_purchases_up_to_time
return self
def fit(self, frequency, recency, T, iterative_fitting=1, initial_params=None, verbose=False, tol=1e-4):
"""
This methods fits the data to the BG/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.
Returns:
self, with additional properties and methods like params_ and predict
"""
frequency = asarray(frequency)
recency = asarray(recency)
T = asarray(T)
_check_inputs(frequency, recency, T)
self._scale = _scale_time(T)
scaled_recency = recency * self._scale
scaled_T = T * self._scale
params, self._negative_log_likelihood_ = _fit(self._negative_log_likelihood,
[frequency, scaled_recency, scaled_T, self.penalizer_coef],
iterative_fitting,
initial_params,
4,
verbose,
tol)
self.params_ = OrderedDict(zip(['r', 'alpha', 'a', 'b'], params))
self.params_['alpha'] /= self._scale
self.data = DataFrame(vconcat[frequency, recency, T], columns=['frequency', 'recency', 'T'])
self.generate_new_data = lambda size=1: beta_geometric_nbd_model(T, *self._unload_params('r', 'alpha', 'a', 'b'), size=size)
self.predict = self.conditional_expected_number_of_purchases_up_to_time
return self
def fit(self,
frequency,
recency,
T,
iterative_fitting=0,
initial_params=None):
"""
This methods fits the data to the BG/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 peformance but possibly
hurt estimates.
Returns:
self, with additional properties and methods like params_ and plot
"""
frequency = np.asarray(frequency)
recency = np.asarray(recency)
T = np.asarray(T)
params, self._negative_log_likelihood_ = _fit(
self._negative_log_likelihood, frequency, recency, T,
iterative_fitting, self.penalizer_coef, initial_params)
self.params_ = dict(zip(['r', 'alpha', 'a', 'b'], params))
self.data = pd.DataFrame(np.c_[frequency, recency, T],
columns=['frequency', 'recency', 'T'])
self.generate_new_data = lambda size=1: beta_geometric_nbd_model(
T, *params, size=size)
self.predict = self.conditional_expected_number_of_purchases_up_to_time
return self
