def test_parametric_plotting_with_show_censors(self, block):
n = 200
T = (np.sqrt(50) * np.random.exponential(1, size=n)) ** 2
E = T < 100
T = np.minimum(T, 100)
wf = WeibullFitter().fit(T, E)
wf.plot_density(show_censors=True)
wf.plot_cumulative_density(show_censors=True)
self.plt.title("test_parametric_plotting_with_show_censors:cumulative_density")
self.plt.show(block=block)
wf.plot_survival_function(show_censors=True)
self.plt.title("test_parametric_plotting_with_show_censors:survival_function")
self.plt.show(block=block)
wf.plot_cumulative_hazard(show_censors=True)
self.plt.title("test_parametric_plotting_with_show_censors:cumulative_hazard")
self.plt.show(block=block)
wf.plot_density(show_censors=True)
self.plt.title("test_parametric_plotting_with_show_censors:density")
self.plt.show(block=block)
return
def test_label_can_be_changed_on_univariate_fitters(self, block):
T = np.random.exponential(5, size=(2000, 1)) ** 2
wf = WeibullFitter().fit(T, timeline=np.linspace(0, 5))
ax = wf.plot_hazard(label="abc")
wf.plot_cumulative_hazard(ax=ax, label="123")
self.plt.title("test_label_can_be_changed_on_univariate_fitters")
self.plt.show(block=block)
return
def test_weibull_plotting(self, block):
T = 50 * np.random.exponential(1, size=(200, 1)) ** 2
wf = WeibullFitter().fit(T, timeline=np.linspace(0, 5, 100))
wf.plot_hazard()
self.plt.title("test_weibull_plotting:hazard")
self.plt.show(block=block)
wf.plot_cumulative_hazard()
self.plt.title("test_weibull_plotting:cumulative_hazard")
self.plt.show(block=block)
return
def test_weibull_plotting(self, block):
T = np.random.exponential(5, size=(2000, 1))**2
wf = WeibullFitter().fit(T)
wf.plot_hazard()
self.plt.title("test_weibull_plotting:hazard")
self.plt.show(block=block)
wf.plot_cumulative_hazard()
self.plt.title("test_weibull_plotting:cumulative_hazard")
self.plt.show(block=block)
return
def test_parametric_plotting_with_show_censors(self, block):
n = 200
T = 50 * np.random.exponential(1, size=(n, 1)) ** 2
E = np.random.rand(n) > 0.2
wf = WeibullFitter().fit(T, E, timeline=np.linspace(0, 5, 1000))
wf.plot_cumulative_density(show_censors=True)
self.plt.title("test_parametric_plotting_with_show_censors:cumulative_density")
self.plt.show(block=block)
wf.plot_survival_function(show_censors=True)
self.plt.title("test_parametric_plotting_with_show_censors:survival_function")
self.plt.show(block=block)
wf.plot_cumulative_hazard(show_censors=True)
self.plt.title("test_parametric_plotting_with_show_censors:cumulative_hazard")
self.plt.show(block=block)
return
data = pd.read_csv('Dataset/telco_customer.csv')
data['tenure'] = pd.to_numeric(data['tenure'])
data = data[data['tenure'] > 0]
# Replace yes and No in the Churn column to 1 and 0. 1 for the event and 0 for the censured data.
data['Churn'] = data['Churn'].apply(lambda x: 1 if x == 'Yes' else 0)
fig, axes = plt.subplots(2, 2, figsize=(16, 12))
T = data['tenure']
E = data['Churn']
wbf = WeibullFitter().fit(T, E, label='WeibullFitter')
ef = ExponentialFitter().fit(T, E, label='ExponentialFitter')
lnf = LogNormalFitter().fit(T, E, label='LogNormalFitter')
llf = LogLogisticFitter().fit(T, E, label='LogLogisticFitter')
wbf.plot_cumulative_hazard(ax=axes[0][0])
ef.plot_cumulative_hazard(ax=axes[0][1])
lnf.plot_cumulative_hazard(ax=axes[1][0])
llf.plot_cumulative_hazard(ax=axes[1][1])
plt.suptitle(
'Parametric Model Implementation of the Telco dataset using different models'
)
fig.text(0.5, 0.04, 'Timeline', ha='center')
fig.text(0.04, 0.5, 'Probability', va='center', rotation='vertical')
plt.savefig('Images/WeiExpLogx.jpeg')
plt.show()