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_logx_plotting(self, block):
waltons = load_waltons()
kmf = KaplanMeierFitter().fit(np.exp(waltons["T"]), waltons["E"], timeline=np.logspace(0, 40))
ax = kmf.plot(logx=True)
wf = WeibullFitter().fit(np.exp(waltons["T"]), waltons["E"], timeline=np.logspace(0, 40))
wf.plot_survival_function(logx=True, ax=ax)
self.plt.title("test_logx_plotting")
self.plt.show(block=block)
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
import pandas as pd
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_survival_function(ax=axes[0][0])
ef.plot_survival_function(ax=axes[0][1])
lnf.plot_survival_function(ax=axes[1][0])
llf.plot_survival_function(ax=axes[1][1])
plt.suptitle(
'Implementation of Paramteric Models to create survival functions on the teleco dataset'
)
fig.text(0.5, 0.04, 'Timeline', ha='center')
fig.text(0.04, 0.5, 'Probability', va='center', rotation='vertical')
plt.savefig('Images/SurvivalFunctions.jpeg')
plt.show()
CI = ((df['CITY'] == 1), "city")
MM = ((df['MONDAY'] == 1), "Monday")
BROKEN = ((df['DEAD'] > 0), "all broken down")
OKAY = ((df['DEAD'] == 0), "all okay") # not used obviously
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 4))
ax.hist(df["KM"], bins=100, rwidth=0.8)
fig0, ax0 = plt.subplots(nrows=1, ncols=1, figsize=(8, 4))
T = df['KM']
E = df['DEAD'] # all broken trucks
wf = WeibullFitter().fit(T, E)
wf.print_summary()
wf.plot_survival_function(ax=ax0, ci_show=False, label="Full population")
kmf = KaplanMeierFitter()
kmf.fit(T, event_observed=E)
kmf.survival_function_
kmf.cumulative_density_
kmf.plot_survival_function(ax=ax0, ci_show=False)
#kmf.plot_cumulative_density()
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8, 4))
for X, x in [BROKEN, EN, MO, CI, MM]:
T = df['KM'][X]
E = df['DEAD'][X]