def get_estimates_from_bootstrap(params, daily_installs, observed_days,
conversion_rate, free_trial_conversion, N):
model = BGModel(penalizer_coef=0.01)
Ts = reduce(
lambda x, y: x + y,
[[(observed_days - day) / 7] *
int(math.floor(installs * conversion_rate * free_trial_conversion))
for day, installs in enumerate(daily_installs)])
Ts = filter(lambda x: x > 0, Ts)
gen_data = gen.bgext_model(Ts, params['alpha'], params['beta'])
data = comp.compress_bgext_data(gen_data)
model.fit(frequency=data["frequency"],
T=data["T"],
N=data["N"],
bootstrap_size=N)
exs = []
for i in range(N):
a = model.sampled_parameters[i]['alpha']
b = model.sampled_parameters[i]['beta']
cov = model.params_C
[a, b] = uncertainties.correlated_values([a, b], cov)
Ex = model.wrapped_static_expected_number_of_purchases_up_to_time(
a, b, 52) + 1
if not math.isnan(Ex.n) and not math.isinf(Ex.n):
print((i, Ex))
exs.append(Ex)
return exs, model.expected_number_of_purchases_up_to_time(52) + 1
def test_BG_fitting_compressed_or_not():
T = 10
size = 1000
params = {'alpha': 0.3, 'beta': 3.7}
data = gen.bgext_model(T, params['alpha'], params['beta'], size=size)
print(data)
compressed_data = compress_bgext_data(data)
fitter = est.BGFitter(penalizer_coef=0.1)
fitter_compressed = est.BGFitter(penalizer_coef=0.1)
fitter.fit(data['frequency'], data['T'], initial_params=params.values())
fitter_compressed.fit(compressed_data['frequency'],
compressed_data['T'],
N=compressed_data['N'],
initial_params=params.values())
print(params)
print(fitter.params_)
print(fitter_compressed.params_)
for par_name in params.keys():
assert math.fabs(fitter.params_[par_name] -
fitter_compressed.params_[par_name]) < 0.00001
def test_goodness_of_test_BG():
params = {'alpha': 0.32, 'beta': 0.85}
gen_data = compress_bgext_data(gen.bgext_model(T=sample_T,
alpha=params['alpha'],
beta=params['beta']))
assert goodness_of_test(gen_data, fitter_class=est.BGFitter, verbose=True)
# clearly not BG
frequency = [0, 1, 2, 3, 4, 5]
T = [5, 5, 5, 5, 5, 5]
N = [10, 13, 17, 30, 40, 40]
non_bg_data = pd.DataFrame({'frequency': frequency, 'T': T, 'N': N})
assert not goodness_of_test(non_bg_data, fitter_class=est.BGFitter, verbose=True)
# borderline BG
frequency = [0, 1, 2, 3, 4, 5]
T = [5, 5, 5, 5, 5, 5]
N = [10, 10, 10, 30, 10, 40]
borderline_bg_data = pd.DataFrame({'frequency': frequency, 'T': T, 'N': N})
assert not goodness_of_test(borderline_bg_data, fitter_class=est.BGFitter, verbose=True)
def test_split_data():
params = {'alpha': 0.32, 'beta': 0.85}
gen_data = compress_bgext_data(gen.bgext_model(T=sample_T,
alpha=params['alpha'],
beta=params['beta']))
gen_data, test_data = split_dataset(gen_data, 0.3)
assert goodness_of_test(gen_data, fitter_class=est.BGFitter, verbose=True, test_data=test_data)
def test_generte_BGExt_for_external_studies():
params = {'alpha': 0.32, 'beta': 0.85}
gen_data = gen.bgext_model(20, params['alpha'], params['beta'], size=10000)
c_gen_data = compress_bgext_data(gen_data)
c_gen_data.to_csv("/Users/marcomeneghelli/Desktop/bg_data_2.csv")
def test_BG_integration_in_models_with_uncertainties():
T = 10
size = 1000
params = {'alpha': 0.17, 'beta': 1.18}
data = gen.bgext_model([1] * 300 + [2] * 200 + [3] * 180 + [4] * 37,
params['alpha'], params['beta']) # , size=size)
data = gen.bgext_model(T, params['alpha'], params['beta'], size=size)
data = compress_bgext_data(data)
model = models.BGModel(penalizer_coef=0.1)
model.fit(data['frequency'],
data['T'],
bootstrap_size=10,
N=data['N'],
initial_params=params.values())
print("Generation params")
print(params)
print("Fitted params")
print(model.params)
print(model.params_C)
print("Uncertain parameters")
print(model.uparams)
print("E[X(t)] as a function of t")
for t in [0, 1, 2, 3, 4, 5, 7, 10, 20, 50, 100, 1000, 10000]:
Ex = model.expected_number_of_purchases_up_to_time(t)
print(t, Ex)
assert Ex.n >= 0
assert Ex.s >= 0
t = 10
print("E[X(t) = n] as a function of n, t = " + str(t))
tot_prob = 0.0
for n in range(t + 1):
prob = model.fitter.probability_of_n_purchases_up_to_time(t, n)
print(n, prob)
tot_prob += prob
assert 1 >= prob >= 0
uprob = model.probability_of_n_purchases_up_to_time(t, n)
print(uprob)
assert is_almost_equal(uprob.n, prob)
assert math.fabs(tot_prob - 1.0) < 0.00001
def test_correlations_of_uparams_and_derivatives():
T = 10
size = 100
params = {'alpha': 0.17, 'beta': 1.18}
data = gen.bgext_model(T, params['alpha'], params['beta'], size=size)
data = compress_bgext_data(data)
model = models.BGModel(penalizer_coef=0.1)
model.fit(data['frequency'],
data['T'],
bootstrap_size=10,
N=data['N'],
initial_params=params.values())
print("Generation params")
print(params)
print("Fitted params")
print(model.params)
print(model.params_C)
print("Uncertain parameters")
print(model.uparams)
assert is_almost_equal(
correlation_matrix([model.uparams['alpha'],
model.uparams['alpha']])[0, 1], 1.0)
assert 1.0 > correlation_matrix([
model.uparams['alpha'] + ufloat(1, 1), model.uparams['alpha']
])[0, 1] > 0.0
# stub of profile
p1 = model.expected_number_of_purchases_up_to_time(1)
p2 = model.expected_number_of_purchases_up_to_time(2)
assert 1.0 > correlation_matrix([p1, p2])[0, 1] > 0.0
# stub of profile
p1 = model.expected_number_of_purchases_up_to_time(1)
p2 = model.expected_number_of_purchases_up_to_time(10)
assert 1.0 > correlation_matrix([p1, p2])[0, 1] > 0.0
# stub of profile
p1 = model.expected_number_of_purchases_up_to_time(1)
p2 = model.expected_number_of_purchases_up_to_time(100)
assert 1.0 > correlation_matrix([p1, p2])[0, 1] > 0.0
def test_generate_BG_neg_likelihoods():
params = {'alpha': 0.32, 'beta': 0.85}
simulation_size = 100
N_users = 1000
gen_data = compress_bgext_data(gen.bgext_model(T=sample_T,
alpha=params['alpha'],
beta=params['beta']))
fitter = est.BGFitter(0.1)
fitter.fit(**gen_data)
n_lls = generate_neg_likelihoods(fitter=fitter, size=N_users, simulation_size=simulation_size)
assert len(n_lls) == simulation_size
assert n_lls.std() > 0
def test_BG_additional_functions():
T = 10
size = 1000
params = {'alpha': 0.3, 'beta': 3.7}
data = gen.bgext_model(T, params['alpha'], params['beta'], size=size)
print(data)
data = compress_bgext_data(data)
fitter = est.BGFitter(penalizer_coef=0.1)
fitter.fit(data['frequency'],
data['T'],
N=data['N'],
initial_params=params.values())
print("Generation params")
print(params)
print("Fitted params")
print(fitter.params_)
print("E[X(t)] as a function of t")
for t in [1, 10, 100, 1000, 10000]:
Ex = fitter.expected_number_of_purchases_up_to_time(t)
covariance_matrix = np.cov(
np.vstack([[(params['alpha'] * 0.1)**2, 0],
[0, (params['beta'] * 0.1)**2]]))
Ex_err = fitter.expected_number_of_purchases_up_to_time_error(
t, covariance_matrix)
print(t, Ex, Ex / t, Ex_err)
assert Ex >= 0
assert Ex_err >= 0
t = 10
print("P[X(t) = n] as a function of n, t = " + str(t))
tot_prob = 0.0
for n in range(t + 1):
prob = fitter.probability_of_n_purchases_up_to_time(t, n)
print(n, prob)
tot_prob += prob
assert 1 >= prob >= 0
assert math.fabs(tot_prob - 1.0) < 0.00001
def test_address_dispersion_of_fit_with_few_renewals():
params = {'alpha': 2.26, 'beta': 8.13} # similar to ReadIt
print("True number of renewals:")
true_Ex = est.BGFitter.static_expected_number_of_purchases_up_to_time(
params['alpha'], params['beta'], 52) + 1
print(true_Ex)
print("Estimates:")
N = 30
conv_day = 8
T = 2
estimates = []
for i in range(N):
gen_data = gen.bgext_model([T - 2, T - 1, T] * (conv_day * 7),
params['alpha'], params['beta'])
data = compress_bgext_data(gen_data)
model = models.BGModel(penalizer_coef=0.1)
model.fit(
data['frequency'],
data['T'],
bootstrap_size=30,
N=data['N'],
) # initial_params=params.values()
# print "Fitted params"
# print model.params
# print model.params_C
Ex = model.expected_number_of_purchases_up_to_time(52) + 1
print(Ex)
estimates.append(Ex.n)
plt.hist(estimates, 50, normed=0, facecolor='g', alpha=0.75)
plt.xlabel('estimates')
plt.title('Histogram of ' + str(N) + ' estimates (true value in red) - ' +
str(conv_day) + ' conv/day, T: ' + str(T))
plt.axvline(x=true_Ex, color="red")
plt.grid(True)
plt.show()
def test_BG_on_simil_real_data():
T = 10
size = 1000
params = {'alpha': 0.17, 'beta': 1.18}
# let's take a case similar to Spy Calc Free:
# data = gen.bgext_model([1] * 300 + [2] * 200 + [3] * 180 + [4] * 37, params['alpha'], params['beta']) #, size=size)
data = gen.bgext_model([1] * 3000 + [2] * 2000 + [3] * 1800 + [4] * 370,
params['alpha'], params['beta']) # , size=size)
# data = pd.read_csv("/Users/marcomeneghelli/Desktop/SCF_data.csv")
data = compress_bgext_data(data)
model = models.BGModel(penalizer_coef=0.1)
model.fit(data['frequency'],
data['T'],
bootstrap_size=10,
N=data['N'],
initial_params=params.values())
print("Generation params")
print(params)
print("Fitted params")
print(model.params)
print(model.params_C)
print("E[X(t)] as a function of t")
for t in [0, 1, 10, 100, 1000, 10000]:
Ex = model.expected_number_of_purchases_up_to_time(t)
print(t, Ex)
assert Ex.n >= 0
assert Ex.s >= 0
t = 10
print("E[X(t) = n] as a function of n, t = " + str(t))
tot_prob = 0.0
for n in range(t + 1):
prob = model.fitter.probability_of_n_purchases_up_to_time(t, n)
print(n, prob)
tot_prob += prob
assert 1 >= prob >= 0
assert math.fabs(tot_prob - 1.0) < 0.00001
def get_estimates(params, daily_installs, observed_days, conversion_rate,
free_trial_conversion, N):
Ts = reduce(
lambda x, y: x + y,
[[(observed_days - day) / 7] *
int(math.floor(installs * conversion_rate * free_trial_conversion))
for day, installs in enumerate(daily_installs)])
Ts = filter(lambda x: x > 0, Ts)
exs = []
for i in range(N):
gen_data = gen.bgext_model(Ts, params['alpha'], params['beta'])
data = comp.compress_bgext_data(gen_data)
model = BGModel(penalizer_coef=0.1)
model.fit(data['frequency'], data['T'], bootstrap_size=30, N=data['N'])
Ex = model.expected_number_of_purchases_up_to_time(52) + 1
print((i, Ex))
exs.append(Ex)
return exs
def test_BGExt_generation():
params = {'alpha': 2.23, 'beta': 9.35}
gen_data = gen.bgext_model(52, params['alpha'], params['beta'], size=1000)
assert len(gen_data) == 1000
assert 'T' in gen_data
assert 'frequency' in gen_data
assert 'theta' in gen_data
print(gen_data)
print(compress_bgext_data(gen_data))
gen_data = gen.bgext_model([5, 5, 1, 1],
params['alpha'],
params['beta'],
size=10)
assert len(gen_data) == 4
assert 'T' in gen_data
assert 'frequency' in gen_data
assert 'theta' in gen_data
print(gen_data)
free_trial_conversion = 0.6
true_Ex = BGModel().fitter.static_expected_number_of_purchases_up_to_time(params['alpha'], params['beta'], 52) + 1
exss = []
fitted_e_x = []
percentiles_e_x = []
observed_days = 20
for n in range(N):
if ((n+1) % 10) == 0:
print(n+1)
Ts = reduce(lambda x, y: x + y,
[[(observed_days - day) / 7] * int(math.floor(installs * conversion_rate * free_trial_conversion))
for day, installs in enumerate(daily_installs)])
Ts = filter(lambda x: x > 0, Ts)
current_model = BGModel()
gen_data = gen.bgext_model(Ts, params['alpha'], params['beta'])
data = comp.compress_bgext_data(gen_data)
current_model.fit(frequency=data["frequency"], T=data["T"], N=data["N"], bootstrap_size=100)
ex = current_model.expected_number_of_purchases_up_to_time(52) + 1
fitted_e_x.append(ex)
percentiles_data = filter(lambda x: not (math.isnan(x) or math.isinf(x)), [BGFitter.static_expected_number_of_purchases_up_to_time(pars['alpha'], pars['beta'], 52) + 1 for pars in current_model.sampled_parameters])
if len(percentiles_data) > 0:
percentiles = (np.percentile(percentiles_data, 16), np.percentile(percentiles_data, 84))
percentiles_e_x.append(percentiles)
# plt.hist(
# percentiles_data,
# bins=range(40), normed=0,
# alpha=0.3
# )
# plt.axvline(x=true_Ex, color='red', alpha =0.7)
# plt.axvline(x=percentiles[0], color='blue', alpha =0.7)
# plt.axvline(x=percentiles[1], color='blue', alpha =0.7)
def test_BG_compression():
params = {'alpha': 2.23, 'beta': 9.35}
gen_data = gen.bgext_model(52, params['alpha'], params['beta'], size=1000)
comp_data = compress_bgext_data(gen_data)
assert len(gen_data) == sum(comp_data['N'])
test_data['N'] = test_N
test_data = test_data[test_data['N'] > 0]
train_data = data.copy(deep=True)
train_data['N'] = train_N
train_data = train_data[train_data['N'] > 0]
return train_data, test_data
if __name__ == "__main__":
params = {'alpha': 0.32, 'beta': 0.85}
gen_data = compress_bgext_data(gen.bgext_model(T=[2] * 1000 + [3] * 1000
+ [4] * 1000 + [5] * 1000
+ [6] * 1000 + [7] * 1000,
alpha=params['alpha'],
beta=params['beta']))
test_n = multinomial_sample(gen_data['N'])
test_data = gen_data.copy(deep=True)
test_data['N'] = test_n
print(goodness_of_test(
data=gen_data,
fitter_class=est.BGFitter,
test_data=test_data,
verbose=True))
# simulation_size = 100
# N_users = 10000
# T_horizon = 10
