def test_Fit_Everything():
dist = Beta_Distribution(alpha=5, beta=4)
rawdata = dist.random_samples(200, seed=5)
data = make_right_censored_data(data=rawdata, threshold=dist.mean)
fit = Fit_Everything(failures=data.failures, right_censored=data.right_censored, show_probability_plot=False,show_histogram_plot=False,show_PP_plot=False,print_results=False)
assert_allclose(fit.best_distribution.alpha, 0.5796887217806559,rtol=rtol,atol=atol)
assert_allclose(fit.best_distribution.beta, 4.205258772699503,rtol=rtol,atol=atol)
assert_allclose(fit.Beta_2P_BIC, 30.739845510058352,rtol=rtol,atol=atol)
def melhor_distribuicao(Falhas):
raw_data = list(Falhas)
data = make_right_censored_data(raw_data,
threshold=1500) # right censor the data
results = Fit_Everything(failures=data.failures,
right_censored=data.right_censored,
show_probability_plot=False,
show_histogram_plot=False,
show_PP_plot=False,
print_results=False,
exclude=[
'Exponential_2P', 'Gamma_3P', 'Gamma_2P',
'Lognormal_3P', 'Loglogistic_3P', 'Gumbel_2P'
])
return results.best_distribution_name, results.best_distribution.parameters
def __init__(self,
distribution,
include_location_shifted=True,
show_plot=True,
print_results=True,
number_of_distributions_to_show=3):
# ensure the input is a distribution object
if type(distribution) not in [
Weibull_Distribution, Normal_Distribution,
Lognormal_Distribution, Exponential_Distribution,
Gamma_Distribution, Beta_Distribution
]:
raise ValueError(
'distribution must be a probability distribution object from the reliability.Distributions module. First define the distribution using Reliability.Distributions.___'
)
# sample the CDF from 0.001 to 0.999. These samples will be used to fit all other distributions.
RVS = distribution.quantile(
np.linspace(0.001, 0.999, 698)
) # 698 samples is the ideal number for the points to align. Evidenced using plot_points.
# filter out negative values
RVS_filtered = []
negative_values_error = False
for item in RVS:
if item > 0:
RVS_filtered.append(item)
else:
negative_values_error = True
if negative_values_error is True:
print(
'WARNING: The input distribution has non-negligible area for x<0. Samples from this region have been discarded to enable other distributions to be fitted.'
)
fitted_results = Fit_Everything(
failures=RVS_filtered,
print_results=False,
show_probability_plot=False,
show_histogram_plot=False,
show_PP_plot=False
) # fit all distributions to the filtered samples
ranked_distributions = list(fitted_results.results.index.values)
ranked_distributions.remove(
distribution.name2
) # removes the fitted version of the original distribution
ranked_distributions_objects = []
ranked_distributions_labels = []
sigfig = 2
for dist_name in ranked_distributions:
if dist_name == 'Weibull_2P':
ranked_distributions_objects.append(
Weibull_Distribution(alpha=fitted_results.Weibull_2P_alpha,
beta=fitted_results.Weibull_2P_beta))
ranked_distributions_labels.append(
str('Weibull_2P (α=' +
str(round(fitted_results.Weibull_2P_alpha, sigfig)) +
',β=' +
str(round(fitted_results.Weibull_2P_beta, sigfig)) +
')'))
elif dist_name == 'Gamma_2P':
ranked_distributions_objects.append(
Gamma_Distribution(alpha=fitted_results.Gamma_2P_alpha,
beta=fitted_results.Gamma_2P_beta))
ranked_distributions_labels.append(
str('Gamma_2P (α=' +
str(round(fitted_results.Gamma_2P_alpha, sigfig)) +
',β=' +
str(round(fitted_results.Gamma_2P_beta, sigfig)) +
')'))
elif dist_name == 'Normal_2P':
ranked_distributions_objects.append(
Normal_Distribution(mu=fitted_results.Normal_2P_mu,
sigma=fitted_results.Normal_2P_sigma))
ranked_distributions_labels.append(
str('Normal_2P (μ=' +
str(round(fitted_results.Normal_2P_mu, sigfig)) +
',σ=' +
str(round(fitted_results.Normal_2P_sigma, sigfig)) +
')'))
elif dist_name == 'Lognormal_2P':
ranked_distributions_objects.append(
Lognormal_Distribution(
mu=fitted_results.Lognormal_2P_mu,
sigma=fitted_results.Lognormal_2P_sigma))
ranked_distributions_labels.append(
str('Lognormal_2P (μ=' +
str(round(fitted_results.Lognormal_2P_mu, sigfig)) +
',σ=' +
str(round(fitted_results.Lognormal_2P_sigma, sigfig)) +
')'))
elif dist_name == 'Exponential_1P':
ranked_distributions_objects.append(
Exponential_Distribution(
Lambda=fitted_results.Expon_1P_lambda))
ranked_distributions_labels.append(
str('Exponential_1P (lambda=' +
str(round(fitted_results.Expon_1P_lambda, sigfig)) +
')'))
elif dist_name == 'Beta_2P':
ranked_distributions_objects.append(
Beta_Distribution(alpha=fitted_results.Beta_2P_alpha,
beta=fitted_results.Beta_2P_beta))
ranked_distributions_labels.append(
str('Beta_2P (α=' +
str(round(fitted_results.Beta_2P_alpha, sigfig)) +
',β=' +
str(round(fitted_results.Beta_2P_beta, sigfig)) + ')'))
if include_location_shifted is True:
if dist_name == 'Weibull_3P':
ranked_distributions_objects.append(
Weibull_Distribution(
alpha=fitted_results.Weibull_3P_alpha,
beta=fitted_results.Weibull_3P_beta,
gamma=fitted_results.Weibull_3P_gamma))
ranked_distributions_labels.append(
str('Weibull_3P (α=' + str(
round(fitted_results.Weibull_3P_alpha, sigfig)) +
',β=' +
str(round(fitted_results.Weibull_3P_beta,
sigfig)) + ',γ=' +
str(round(fitted_results.Weibull_3P_gamma,
sigfig)) + ')'))
elif dist_name == 'Gamma_3P':
ranked_distributions_objects.append(
Gamma_Distribution(
alpha=fitted_results.Gamma_3P_alpha,
beta=fitted_results.Gamma_3P_beta,
gamma=fitted_results.Gamma_3P_gamma))
ranked_distributions_labels.append(
str('Gamma_3P (α=' +
str(round(fitted_results.Gamma_3P_alpha, sigfig)) +
',β=' +
str(round(fitted_results.Gamma_3P_beta, sigfig)) +
',γ=' +
str(round(fitted_results.Gamma_3P_gamma, sigfig)) +
')'))
elif dist_name == 'Lognormal_3P':
ranked_distributions_objects.append(
Lognormal_Distribution(
mu=fitted_results.Lognormal_3P_mu,
sigma=fitted_results.Lognormal_3P_sigma,
gamma=fitted_results.Lognormal_3P_gamma))
ranked_distributions_labels.append(
str('Lognormal_3P (μ=' + str(
round(fitted_results.Lognormal_3P_mu, sigfig)) +
',σ=' + str(
round(fitted_results.Lognormal_3P_sigma,
sigfig)) + ',γ=' +
str(
round(fitted_results.Lognormal_3P_gamma,
sigfig)) + ')'))
elif dist_name == 'Exponential_2P':
ranked_distributions_objects.append(
Exponential_Distribution(
Lambda=fitted_results.Expon_1P_lambda,
gamma=fitted_results.Expon_2P_gamma))
ranked_distributions_labels.append(
str('Exponential_1P (lambda=' + str(
round(fitted_results.Expon_1P_lambda, sigfig)) +
',γ=' +
str(round(fitted_results.Expon_2P_gamma, sigfig)) +
')'))
number_of_distributions_fitted = len(ranked_distributions_objects)
self.results = ranked_distributions_objects
self.most_similar_distribution = ranked_distributions_objects[0]
if print_results is True:
print('The input distribution was:')
print(distribution.param_title_long)
if number_of_distributions_fitted < number_of_distributions_to_show:
number_of_distributions_to_show = number_of_distributions_fitted
print('\nThe top', number_of_distributions_to_show,
'most similar distributions are:')
counter = 0
while counter < number_of_distributions_to_show and counter < number_of_distributions_fitted:
dist = ranked_distributions_objects[counter]
print(dist.param_title_long)
counter += 1
if show_plot is True:
plt.figure(figsize=(14, 6))
plt.suptitle(
str('Plot of similar distributions to ' +
distribution.param_title_long))
counter = 0
xlower = distribution.quantile(0.001)
xupper = distribution.quantile(0.999)
x_delta = xupper - xlower
plt.subplot(121)
distribution.PDF(label=str('Input distribution [' +
distribution.name2 + ']'),
linestyle='--')
while counter < number_of_distributions_to_show and counter < number_of_distributions_fitted:
ranked_distributions_objects[counter].PDF(
label=ranked_distributions_labels[counter])
counter += 1
plt.xlim([xlower - x_delta * 0.1, xupper + x_delta * 0.1])
plt.legend()
plt.title('PDF')
counter = 0
plt.subplot(122)
distribution.CDF(label=str('Input distribution [' +
distribution.name2 + ']'),
linestyle='--')
while counter < number_of_distributions_to_show and counter < number_of_distributions_fitted:
ranked_distributions_objects[counter].CDF(
label=ranked_distributions_labels[counter])
counter += 1
plt.xlim([xlower - x_delta * 0.1, xupper + x_delta * 0.1])
plt.legend()
plt.title('CDF')
plt.subplots_adjust(left=0.08, right=0.95)
plt.show()
def test_Fit_Everything():
dist = Beta_Distribution(alpha=5, beta=4)
rawdata = dist.random_samples(200, seed=5)
data = make_right_censored_data(data=rawdata, threshold=dist.mean)
MLE = Fit_Everything(failures=data.failures, right_censored=data.right_censored, method='MLE', show_probability_plot=False, show_histogram_plot=False, show_PP_plot=False, show_best_distribution_probability_plot=False, print_results=False)
LS = Fit_Everything(failures=data.failures, right_censored=data.right_censored, method='LS', show_probability_plot=False, show_histogram_plot=False, show_PP_plot=False, show_best_distribution_probability_plot=False, print_results=False)
assert_allclose(MLE.best_distribution.alpha, 0.5796887225805948, rtol=rtol, atol=atol) # best fit here is a Beta distribution
assert_allclose(MLE.best_distribution.beta, 4.205258710807067, rtol=rtol, atol=atol)
assert_allclose(MLE.Weibull_2P_alpha, 0.5796887225805948, rtol=rtol, atol=atol)
assert_allclose(MLE.Weibull_2P_beta, 4.205258710807067, rtol=rtol, atol=atol)
assert_allclose(MLE.Weibull_2P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(MLE.Weibull_2P_AICc, 22.509958498975394, rtol=rtol, atol=atol)
assert_allclose(MLE.Weibull_2P_BIC, 29.04567952648771, rtol=rtol, atol=atol)
assert_allclose(MLE.Weibull_2P_loglik, -9.224522396695818, rtol=rtol, atol=atol)
assert_allclose(MLE.Weibull_2P_AD, 543.31193295208, rtol=rtol, atol=atol)
assert_allclose(MLE.Weibull_3P_alpha, 0.5796887225805948, rtol=rtol, atol=atol)
assert_allclose(MLE.Weibull_3P_beta, 4.205258710807067, rtol=rtol, atol=atol)
assert_allclose(MLE.Weibull_3P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(MLE.Weibull_3P_AICc, 24.571493772983473, rtol=rtol, atol=atol)
assert_allclose(MLE.Weibull_3P_BIC, 34.343996893035744, rtol=rtol, atol=atol)
assert_allclose(MLE.Weibull_3P_loglik, -9.224522396695818, rtol=rtol, atol=atol)
assert_allclose(MLE.Weibull_3P_AD, 543.31193295208, rtol=rtol, atol=atol)
assert_allclose(MLE.Gamma_2P_alpha, 0.06343366643685251, rtol=rtol, atol=atol)
assert_allclose(MLE.Gamma_2P_beta, 8.730724670235508, rtol=rtol, atol=atol)
assert_allclose(MLE.Gamma_2P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(MLE.Gamma_2P_AICc, 29.72088918292124, rtol=rtol, atol=atol)
assert_allclose(MLE.Gamma_2P_BIC, 36.25661021043356, rtol=rtol, atol=atol)
assert_allclose(MLE.Gamma_2P_loglik, -12.829987738668741, rtol=rtol, atol=atol)
assert_allclose(MLE.Gamma_2P_AD, 543.5598195358288, rtol=rtol, atol=atol)
assert_allclose(MLE.Gamma_3P_alpha, 0.06343366643685251, rtol=rtol, atol=atol)
assert_allclose(MLE.Gamma_3P_beta, 8.730724670235508, rtol=rtol, atol=atol)
assert_allclose(MLE.Gamma_3P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(MLE.Gamma_3P_AICc, 31.78242445692932, rtol=rtol, atol=atol)
assert_allclose(MLE.Gamma_3P_BIC, 41.55492757698159, rtol=rtol, atol=atol)
assert_allclose(MLE.Gamma_3P_loglik, -12.829987738668741, rtol=rtol, atol=atol)
assert_allclose(MLE.Gamma_3P_AD, 543.5598195358288, rtol=rtol, atol=atol)
assert_allclose(MLE.Loglogistic_2P_alpha, 0.5327695781726263, rtol=rtol, atol=atol)
assert_allclose(MLE.Loglogistic_2P_beta, 4.959959950671738, rtol=rtol, atol=atol)
assert_allclose(MLE.Loglogistic_2P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(MLE.Loglogistic_2P_AICc, 26.2468431389576, rtol=rtol, atol=atol)
assert_allclose(MLE.Loglogistic_2P_BIC, 32.78256416646992, rtol=rtol, atol=atol)
assert_allclose(MLE.Loglogistic_2P_loglik, -11.092964716686922, rtol=rtol, atol=atol)
assert_allclose(MLE.Loglogistic_2P_AD, 543.3968941075816, rtol=rtol, atol=atol)
assert_allclose(MLE.Loglogistic_3P_alpha, 0.5327695781726263, rtol=rtol, atol=atol)
assert_allclose(MLE.Loglogistic_3P_beta, 4.959959950671738, rtol=rtol, atol=atol)
assert_allclose(MLE.Loglogistic_3P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(MLE.Loglogistic_3P_AICc, 28.30837841296568, rtol=rtol, atol=atol)
assert_allclose(MLE.Loglogistic_3P_BIC, 38.08088153301795, rtol=rtol, atol=atol)
assert_allclose(MLE.Loglogistic_3P_loglik, -11.092964716686922, rtol=rtol, atol=atol)
assert_allclose(MLE.Loglogistic_3P_AD, 543.3968941075816, rtol=rtol, atol=atol)
assert_allclose(MLE.Lognormal_2P_mu, -0.6258670209896524, rtol=rtol, atol=atol)
assert_allclose(MLE.Lognormal_2P_sigma, 0.3859306240146529, rtol=rtol, atol=atol)
assert_allclose(MLE.Lognormal_2P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(MLE.Lognormal_2P_AICc, 36.58934382876143, rtol=rtol, atol=atol)
assert_allclose(MLE.Lognormal_2P_BIC, 43.125064856273745, rtol=rtol, atol=atol)
assert_allclose(MLE.Lognormal_2P_loglik, -16.264215061588835, rtol=rtol, atol=atol)
assert_allclose(MLE.Lognormal_2P_AD, 543.7578077426027, rtol=rtol, atol=atol)
assert_allclose(MLE.Lognormal_3P_mu, -0.6258670209896524, rtol=rtol, atol=atol)
assert_allclose(MLE.Lognormal_3P_sigma, 0.3859306240146529, rtol=rtol, atol=atol)
assert_allclose(MLE.Lognormal_3P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(MLE.Lognormal_3P_AICc, 38.65087910276951, rtol=rtol, atol=atol)
assert_allclose(MLE.Lognormal_3P_BIC, 48.42338222282178, rtol=rtol, atol=atol)
assert_allclose(MLE.Lognormal_3P_loglik, -16.264215061588835, rtol=rtol, atol=atol)
assert_allclose(MLE.Lognormal_3P_AD, 543.7578077426027, rtol=rtol, atol=atol)
assert_allclose(MLE.Normal_2P_mu, 0.5313204293962966, rtol=rtol, atol=atol)
assert_allclose(MLE.Normal_2P_sigma, 0.14842166096827056, rtol=rtol, atol=atol)
assert_allclose(MLE.Normal_2P_AICc, 23.0363966340782, rtol=rtol, atol=atol)
assert_allclose(MLE.Normal_2P_BIC, 29.572117661590518, rtol=rtol, atol=atol)
assert_allclose(MLE.Normal_2P_loglik, -9.487741464247222, rtol=rtol, atol=atol)
assert_allclose(MLE.Normal_2P_AD, 543.3042437249142, rtol=rtol, atol=atol)
assert_allclose(MLE.Gumbel_2P_mu, 0.5706624792367315, rtol=rtol, atol=atol)
assert_allclose(MLE.Gumbel_2P_sigma, 0.10182903954122995, rtol=rtol, atol=atol)
assert_allclose(MLE.Gumbel_2P_AICc, 26.09054970134011, rtol=rtol, atol=atol)
assert_allclose(MLE.Gumbel_2P_BIC, 32.626270728852425, rtol=rtol, atol=atol)
assert_allclose(MLE.Gumbel_2P_loglik, -11.014817997878176, rtol=rtol, atol=atol)
assert_allclose(MLE.Gumbel_2P_AD, 543.3089024789034, rtol=rtol, atol=atol)
assert_allclose(MLE.Beta_2P_alpha, 5.586642953718748, rtol=rtol, atol=atol)
assert_allclose(MLE.Beta_2P_beta, 4.950693419749502, rtol=rtol, atol=atol)
assert_allclose(MLE.Beta_2P_AICc, 24.204124482547897, rtol=rtol, atol=atol)
assert_allclose(MLE.Beta_2P_BIC, 30.739845510060213, rtol=rtol, atol=atol)
assert_allclose(MLE.Beta_2P_loglik, -10.07160538848207, rtol=rtol, atol=atol)
assert_allclose(MLE.Beta_2P_AD, 543.3809275359781, rtol=rtol, atol=atol)
assert_allclose(MLE.Exponential_2P_lambda, 1.5845505775713558, rtol=rtol, atol=atol)
assert_allclose(MLE.Exponential_2P_gamma, 0.12428161981215716, rtol=rtol, atol=atol)
assert_allclose(MLE.Exponential_2P_AICc, 127.11230931613672, rtol=rtol, atol=atol)
assert_allclose(MLE.Exponential_2P_BIC, 133.64803034364903, rtol=rtol, atol=atol)
assert_allclose(MLE.Exponential_2P_loglik, -61.52569780527648, rtol=rtol, atol=atol)
assert_allclose(MLE.Exponential_2P_AD, 548.8966650502098, rtol=rtol, atol=atol)
assert_allclose(MLE.Exponential_1P_lambda, 1.1776736956890317, rtol=rtol, atol=atol)
assert_allclose(MLE.Exponential_1P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(MLE.Exponential_1P_AICc, 192.73284561137785, rtol=rtol, atol=atol)
assert_allclose(MLE.Exponential_1P_BIC, 196.01096095772388, rtol=rtol, atol=atol)
assert_allclose(MLE.Exponential_1P_loglik, -95.35632179558792, rtol=rtol, atol=atol)
assert_allclose(MLE.Exponential_1P_AD, 551.326873807673, rtol=rtol, atol=atol)
assert_allclose(LS.best_distribution.mu, 0.5350756091376212, rtol=rtol, atol=atol) # best fit here is a Normal distribution
assert_allclose(LS.best_distribution.sigma, 0.15352298167936318, rtol=rtol, atol=atol)
assert_allclose(LS.Weibull_2P_alpha, 0.5948490848650297, rtol=rtol, atol=atol)
assert_allclose(LS.Weibull_2P_beta, 3.850985192722524, rtol=rtol, atol=atol)
assert_allclose(LS.Weibull_2P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(LS.Weibull_2P_AICc, 24.002343535956285, rtol=rtol, atol=atol)
assert_allclose(LS.Weibull_2P_BIC, 30.538064563468602, rtol=rtol, atol=atol)
assert_allclose(LS.Weibull_2P_loglik, -9.970714915186264, rtol=rtol, atol=atol)
assert_allclose(LS.Weibull_2P_AD, 543.3536598333712, rtol=rtol, atol=atol)
assert_allclose(LS.Weibull_3P_alpha, 0.5796887225805948, rtol=rtol, atol=atol)
assert_allclose(LS.Weibull_3P_beta, 4.205258710807067, rtol=rtol, atol=atol)
assert_allclose(LS.Weibull_3P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(LS.Weibull_3P_AICc, 24.571493772983473, rtol=rtol, atol=atol)
assert_allclose(LS.Weibull_3P_BIC, 34.343996893035744, rtol=rtol, atol=atol)
assert_allclose(LS.Weibull_3P_loglik, -9.224522396695818, rtol=rtol, atol=atol)
assert_allclose(LS.Weibull_3P_AD, 543.31193295208, rtol=rtol, atol=atol)
assert_allclose(LS.Gamma_2P_alpha, 0.047474493713487956, rtol=rtol, atol=atol)
assert_allclose(LS.Gamma_2P_beta, 11.56120649983023, rtol=rtol, atol=atol)
assert_allclose(LS.Gamma_2P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(LS.Gamma_2P_AICc, 34.77520772749797, rtol=rtol, atol=atol)
assert_allclose(LS.Gamma_2P_BIC, 41.31092875501029, rtol=rtol, atol=atol)
assert_allclose(LS.Gamma_2P_loglik, -15.357147010957107, rtol=rtol, atol=atol)
assert_allclose(LS.Gamma_2P_AD, 543.5555679280225, rtol=rtol, atol=atol)
assert_allclose(LS.Gamma_3P_alpha, 0.06343366643685251, rtol=rtol, atol=atol)
assert_allclose(LS.Gamma_3P_beta, 8.730724670235508, rtol=rtol, atol=atol)
assert_allclose(LS.Gamma_3P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(LS.Gamma_3P_AICc, 31.78242445692932, rtol=rtol, atol=atol)
assert_allclose(LS.Gamma_3P_BIC, 41.55492757698159, rtol=rtol, atol=atol)
assert_allclose(LS.Gamma_3P_loglik, -12.829987738668741, rtol=rtol, atol=atol)
assert_allclose(LS.Gamma_3P_AD, 543.5598195358288, rtol=rtol, atol=atol)
assert_allclose(LS.Loglogistic_2P_alpha, 0.5489258630949324, rtol=rtol, atol=atol)
assert_allclose(LS.Loglogistic_2P_beta, 4.282869717868545, rtol=rtol, atol=atol)
assert_allclose(LS.Loglogistic_2P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(LS.Loglogistic_2P_AICc, 29.55884374185365, rtol=rtol, atol=atol)
assert_allclose(LS.Loglogistic_2P_BIC, 36.09456476936597, rtol=rtol, atol=atol)
assert_allclose(LS.Loglogistic_2P_loglik, -12.748965018134946, rtol=rtol, atol=atol)
assert_allclose(LS.Loglogistic_2P_AD, 543.4725652046802, rtol=rtol, atol=atol)
assert_allclose(LS.Loglogistic_3P_alpha, 0.5327695781726263, rtol=rtol, atol=atol)
assert_allclose(LS.Loglogistic_3P_beta, 4.959959950671738, rtol=rtol, atol=atol)
assert_allclose(LS.Loglogistic_3P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(LS.Loglogistic_3P_AICc, 28.30837841296568, rtol=rtol, atol=atol)
assert_allclose(LS.Loglogistic_3P_BIC, 38.08088153301795, rtol=rtol, atol=atol)
assert_allclose(LS.Loglogistic_3P_loglik, -11.092964716686922, rtol=rtol, atol=atol)
assert_allclose(LS.Loglogistic_3P_AD, 543.3968941075816, rtol=rtol, atol=atol)
assert_allclose(LS.Lognormal_2P_mu, -0.5829545855241497, rtol=rtol, atol=atol)
assert_allclose(LS.Lognormal_2P_sigma, 0.42938026719038264, rtol=rtol, atol=atol)
assert_allclose(LS.Lognormal_2P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(LS.Lognormal_2P_AICc, 39.2494098877054, rtol=rtol, atol=atol)
assert_allclose(LS.Lognormal_2P_BIC, 45.785130915217714, rtol=rtol, atol=atol)
assert_allclose(LS.Lognormal_2P_loglik, -17.59424809106082, rtol=rtol, atol=atol)
assert_allclose(LS.Lognormal_2P_AD, 543.6895545238489, rtol=rtol, atol=atol)
assert_allclose(LS.Lognormal_3P_mu, -0.6258670209896524, rtol=rtol, atol=atol)
assert_allclose(LS.Lognormal_3P_sigma, 0.3859306240146529, rtol=rtol, atol=atol)
assert_allclose(LS.Lognormal_3P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(LS.Lognormal_3P_AICc, 38.65087910276951, rtol=rtol, atol=atol)
assert_allclose(LS.Lognormal_3P_BIC, 48.42338222282178, rtol=rtol, atol=atol)
assert_allclose(LS.Lognormal_3P_loglik, -16.264215061588835, rtol=rtol, atol=atol)
assert_allclose(LS.Lognormal_3P_AD, 543.7578077426027, rtol=rtol, atol=atol)
assert_allclose(LS.Normal_2P_mu, 0.5350756091376212, rtol=rtol, atol=atol)
assert_allclose(LS.Normal_2P_sigma, 0.15352298167936318, rtol=rtol, atol=atol)
assert_allclose(LS.Normal_2P_AICc, 23.270071653194492, rtol=rtol, atol=atol)
assert_allclose(LS.Normal_2P_BIC, 29.80579268070681, rtol=rtol, atol=atol)
assert_allclose(LS.Normal_2P_loglik, -9.604578973805367, rtol=rtol, atol=atol)
assert_allclose(LS.Normal_2P_AD, 543.3018089629097, rtol=rtol, atol=atol)
assert_allclose(LS.Gumbel_2P_mu, 0.5575543755580943, rtol=rtol, atol=atol)
assert_allclose(LS.Gumbel_2P_sigma, 0.09267958281580514, rtol=rtol, atol=atol)
assert_allclose(LS.Gumbel_2P_AICc, 28.66352107358925, rtol=rtol, atol=atol)
assert_allclose(LS.Gumbel_2P_BIC, 35.19924210110157, rtol=rtol, atol=atol)
assert_allclose(LS.Gumbel_2P_loglik, -12.301303684002747, rtol=rtol, atol=atol)
assert_allclose(LS.Gumbel_2P_AD, 543.3456378838292, rtol=rtol, atol=atol)
assert_allclose(LS.Beta_2P_alpha, 6.54242621734743, rtol=rtol, atol=atol)
assert_allclose(LS.Beta_2P_beta, 5.795236872686599, rtol=rtol, atol=atol)
assert_allclose(LS.Beta_2P_AICc, 25.745158997195162, rtol=rtol, atol=atol)
assert_allclose(LS.Beta_2P_BIC, 32.28088002470748, rtol=rtol, atol=atol)
assert_allclose(LS.Beta_2P_loglik, -10.842122645805702, rtol=rtol, atol=atol)
assert_allclose(LS.Beta_2P_AD, 543.3718252593867, rtol=rtol, atol=atol)
assert_allclose(LS.Exponential_2P_lambda, 1.1858797968873822, rtol=rtol, atol=atol)
assert_allclose(LS.Exponential_2P_gamma, 0.12338161981215715, rtol=rtol, atol=atol)
assert_allclose(LS.Exponential_2P_AICc, 136.25275877909922, rtol=rtol, atol=atol)
assert_allclose(LS.Exponential_2P_BIC, 142.78847980661155, rtol=rtol, atol=atol)
assert_allclose(LS.Exponential_2P_loglik, -66.09592253675774, rtol=rtol, atol=atol)
assert_allclose(LS.Exponential_2P_AD, 546.5849877012892, rtol=rtol, atol=atol)
assert_allclose(LS.Exponential_1P_lambda, 1.0678223705385204, rtol=rtol, atol=atol)
assert_allclose(LS.Exponential_1P_gamma, 0, rtol=rtol, atol=atol)
assert_allclose(LS.Exponential_1P_AICc, 193.7910857336068, rtol=rtol, atol=atol)
assert_allclose(LS.Exponential_1P_BIC, 197.06920107995282, rtol=rtol, atol=atol)
assert_allclose(LS.Exponential_1P_loglik, -95.88544185670239, rtol=rtol, atol=atol)
assert_allclose(LS.Exponential_1P_AD, 549.85986679373, rtol=rtol, atol=atol)
def __init__(self,
distribution=None,
include_location_shifted=True,
show_plot=True,
print_results=True,
monte_carlo_trials=1000,
number_of_distributions_to_show=3):
if type(distribution) not in [
Weibull_Distribution, Normal_Distribution,
Lognormal_Distribution, Exponential_Distribution,
Gamma_Distribution, Beta_Distribution
]:
raise ValueError(
'distribution must be a probability distribution object from the reliability.Distributions module. First define the distribution using Reliability.Distributions.___'
)
if monte_carlo_trials < 100:
print(
'WARNING: Using less than 100 monte carlo trials will lead to extremely inaccurate results. The number of monte carlo trials has been changed to 100 to ensure accuracy.'
)
monte_carlo_trials = 100
elif monte_carlo_trials >= 100 and monte_carlo_trials < 1000:
print(
'WARNING: Using less than 1000 monte carlo trials will lead to inaccurate results.'
)
if monte_carlo_trials > 10000:
print(
'The recommended number of monte carlo trials is 1000. Using over 10000 may take a long time to calculate.'
)
RVS = distribution.random_samples(
number_of_samples=monte_carlo_trials
) # draw random samples from the original distribution
# filter out negative values
RVS_filtered = []
negative_values_error = False
for item in RVS:
if item > 0:
RVS_filtered.append(item)
else:
negative_values_error = True
if negative_values_error is True:
print(
'WARNING: The input distribution has non-negligible area for x<0. Monte carlo samples from this region have been discarded to enable other distributions to be fitted.'
)
fitted_results = Fit_Everything(
failures=RVS_filtered,
print_results=False,
show_probability_plot=False,
show_histogram_plot=False,
show_PP_plot=False
) # fit all distributions to the filtered samples
ranked_distributions = list(fitted_results.results.index.values)
ranked_distributions.remove(
distribution.name2
) # removes the fitted version of the original distribution
ranked_distributions_objects = []
ranked_distributions_labels = []
sigfig = 2
for dist_name in ranked_distributions:
if dist_name == 'Weibull_2P':
ranked_distributions_objects.append(
Weibull_Distribution(alpha=fitted_results.Weibull_2P_alpha,
beta=fitted_results.Weibull_2P_beta))
ranked_distributions_labels.append(
str('Weibull_2P (α=' +
str(round(fitted_results.Weibull_2P_alpha, sigfig)) +
',β=' +
str(round(fitted_results.Weibull_2P_beta, sigfig)) +
')'))
elif dist_name == 'Gamma_2P':
ranked_distributions_objects.append(
Gamma_Distribution(alpha=fitted_results.Gamma_2P_alpha,
beta=fitted_results.Gamma_2P_beta))
ranked_distributions_labels.append(
str('Gamma_2P (α=' +
str(round(fitted_results.Gamma_2P_alpha, sigfig)) +
',β=' +
str(round(fitted_results.Gamma_2P_beta, sigfig)) +
')'))
elif dist_name == 'Normal_2P':
ranked_distributions_objects.append(
Normal_Distribution(mu=fitted_results.Normal_2P_mu,
sigma=fitted_results.Normal_2P_sigma))
ranked_distributions_labels.append(
str('Normal_2P (μ=' +
str(round(fitted_results.Normal_2P_mu, sigfig)) +
',σ=' +
str(round(fitted_results.Normal_2P_sigma, sigfig)) +
')'))
elif dist_name == 'Lognormal_2P':
ranked_distributions_objects.append(
Lognormal_Distribution(
mu=fitted_results.Lognormal_2P_mu,
sigma=fitted_results.Lognormal_2P_sigma))
ranked_distributions_labels.append(
str('Lognormal_2P (μ=' +
str(round(fitted_results.Lognormal_2P_mu, sigfig)) +
',σ=' +
str(round(fitted_results.Lognormal_2P_sigma, sigfig)) +
')'))
elif dist_name == 'Exponential_1P':
ranked_distributions_objects.append(
Exponential_Distribution(
Lambda=fitted_results.Expon_1P_lambda))
ranked_distributions_labels.append(
str('Exponential_1P (lambda=' +
str(round(fitted_results.Expon_1P_lambda, sigfig)) +
')'))
elif dist_name == 'Beta_2P':
ranked_distributions_objects.append(
Beta_Distribution(alpha=fitted_results.Beta_2P_alpha,
beta=fitted_results.Beta_2P_beta))
ranked_distributions_labels.append(
str('Beta_2P (α=' +
str(round(fitted_results.Beta_2P_alpha, sigfig)) +
',β=' +
str(round(fitted_results.Beta_2P_beta, sigfig)) + ')'))
if include_location_shifted is True:
if dist_name == 'Weibull_3P':
ranked_distributions_objects.append(
Weibull_Distribution(
alpha=fitted_results.Weibull_3P_alpha,
beta=fitted_results.Weibull_3P_beta,
gamma=fitted_results.Weibull_3P_gamma))
ranked_distributions_labels.append(
str('Weibull_3P (α=' + str(
round(fitted_results.Weibull_3P_alpha, sigfig)) +
',β=' +
str(round(fitted_results.Weibull_3P_beta,
sigfig)) + ',γ=' +
str(round(fitted_results.Weibull_3P_gamma,
sigfig)) + ')'))
elif dist_name == 'Gamma_3P':
ranked_distributions_objects.append(
Gamma_Distribution(
alpha=fitted_results.Gamma_3P_alpha,
beta=fitted_results.Gamma_3P_beta,
gamma=fitted_results.Gamma_3P_gamma))
ranked_distributions_labels.append(
str('Gamma_3P (α=' +
str(round(fitted_results.Gamma_3P_alpha, sigfig)) +
',β=' +
str(round(fitted_results.Gamma_3P_beta, sigfig)) +
',γ=' +
str(round(fitted_results.Gamma_3P_gamma, sigfig)) +
')'))
elif dist_name == 'Lognormal_3P':
ranked_distributions_objects.append(
Lognormal_Distribution(
mu=fitted_results.Lognormal_3P_mu,
sigma=fitted_results.Lognormal_3P_sigma,
gamma=fitted_results.Lognormal_3P_gamma))
ranked_distributions_labels.append(
str('Lognormal_3P (μ=' + str(
round(fitted_results.Lognormal_3P_mu, sigfig)) +
',σ=' + str(
round(fitted_results.Lognormal_3P_sigma,
sigfig)) + ',γ=' +
str(
round(fitted_results.Lognormal_3P_gamma,
sigfig)) + ')'))
elif dist_name == 'Exponential_2P':
ranked_distributions_objects.append(
Exponential_Distribution(
Lambda=fitted_results.Expon_1P_lambda,
gamma=fitted_results.Expon_2P_gamma))
ranked_distributions_labels.append(
str('Exponential_1P (lambda=' + str(
round(fitted_results.Expon_1P_lambda, sigfig)) +
',γ=' +
str(round(fitted_results.Expon_2P_gamma, sigfig)) +
')'))
number_of_distributions_fitted = len(ranked_distributions_objects)
self.results = ranked_distributions_objects
self.most_similar_distribution = ranked_distributions_objects[0]
if print_results is True:
print('The input distribution was:')
print(distribution.param_title_long)
if number_of_distributions_fitted < number_of_distributions_to_show:
number_of_distributions_to_show = number_of_distributions_fitted
print('\nThe top', number_of_distributions_to_show,
'most similar distributions are:')
counter = 0
while counter < number_of_distributions_to_show and counter < number_of_distributions_fitted:
dist = ranked_distributions_objects[counter]
print(dist.param_title_long)
counter += 1
if show_plot is True:
plt.figure(figsize=(14, 6))
plt.suptitle(
str('Plot of similar distributions to ' +
distribution.param_title_long))
counter = 0
xlower = distribution.quantile(0.001)
xupper = distribution.quantile(0.999)
x_delta = xupper - xlower
plt.subplot(121)
distribution.PDF(label='Input distribution', linestyle='--')
while counter < number_of_distributions_to_show and counter < number_of_distributions_fitted:
ranked_distributions_objects[counter].PDF(
label=ranked_distributions_labels[counter])
counter += 1
plt.xlim([xlower - x_delta * 0.1, xupper + x_delta * 0.1])
plt.legend()
plt.title('PDF')
counter = 0
plt.subplot(122)
distribution.CDF(label='Input distribution', linestyle='--')
while counter < number_of_distributions_to_show and counter < number_of_distributions_fitted:
ranked_distributions_objects[counter].CDF(
label=ranked_distributions_labels[counter])
counter += 1
plt.xlim([xlower - x_delta * 0.1, xupper + x_delta * 0.1])
plt.legend()
plt.title('CDF')
plt.subplots_adjust(left=0.08, right=0.95)
plt.show()
# Print error message if at least one negative failure time is present for the given component
if error == True:
print(
f"Error: {df.columns[i]} has at least one negative failure time. Negative values are not accepted. The analysis cannot move forward."
)
# Perform reliability analysis
if error == False:
# Print component being analyzed
print(f"Reliability analysis {df.columns[i]}:\n")
# Fit all probability distributions available from 'reliability' library
output = Fit_Everything(failures=df.iloc[:, i].dropna().tolist(),
show_probability_plot=False,
show_PP_plot=False)
# Define the probability distribution that best fitted the failure times for the given component
output.best_distribution.plot()
# Define the desired time of failure 't'
t = float(input("Type in the desired time before failure: "))
# Time 't' validation
# Validate that no negative time was inserted
while t < 0:
print(
"Error: negative value insterted. Please insert a positive value greater than 0:"
)
t = float(input("Type in the desired time before failure: "))