def test_coxph_plot_partial_effects_on_outcome_with_single_strata(self, block):
df = load_rossi()
cp = CoxPHFitter()
cp.fit(df, "week", "arrest", strata="paro")
cp.plot_partial_effects_on_outcome("age", [10, 50, 80])
self.plt.title("test_coxph_plot_partial_effects_on_outcome_with_strata")
self.plt.show(block=block)
def test_coxph_plot_partial_effects_on_outcome_with_multiple_variables(self, block):
df = load_rossi()
cp = CoxPHFitter()
cp.fit(df, "week", "arrest")
cp.plot_partial_effects_on_outcome(["age", "prio"], [[10, 0], [50, 10], [80, 90]])
self.plt.title("test_coxph_plot_partial_effects_on_outcome_with_multiple_variables")
self.plt.show(block=block)
def test_spline_coxph_plot_partial_effects_on_outcome_with_strata(self, block):
df = load_rossi()
cp = CoxPHFitter(baseline_estimation_method="spline", n_baseline_knots=2)
cp.fit(df, "week", "arrest", strata=["wexp"])
cp.plot_partial_effects_on_outcome("age", [10, 50, 80])
self.plt.title("test_spline_coxph_plot_partial_effects_on_outcome_with_strata")
self.plt.show(block=block)
def test_coxph_plot_partial_effects_on_outcome_with_cumulative_hazard(self, block):
df = load_rossi()
cp = CoxPHFitter()
cp.fit(df, "week", "arrest")
cp.plot_partial_effects_on_outcome("age", [10, 50, 80], y="cumulative_hazard")
self.plt.title("test_coxph_plot_partial_effects_on_outcome")
self.plt.show(block=block)
def test_coxph_plot_partial_effects_on_outcome_with_strata_and_complicated_dtypes(
self, block):
# from https://github.com/CamDavidsonPilon/lifelines/blob/master/examples/Customer%20Churn.ipynb
churn_data = pd.read_csv("https://raw.githubusercontent.com/"
"treselle-systems/customer_churn_analysis/"
"master/WA_Fn-UseC_-Telco-Customer-Churn.csv")
churn_data = churn_data.set_index("customerID")
churn_data = churn_data.drop(["TotalCharges"], axis=1)
churn_data = churn_data.applymap(lambda x: "No"
if str(x).startswith("No ") else x)
churn_data["Churn"] = churn_data["Churn"] == "Yes"
strata_cols = ["InternetService"]
cph = CoxPHFitter().fit(
churn_data,
"tenure",
"Churn",
formula=
"gender + SeniorCitizen + Partner + Dependents + MultipleLines + OnlineSecurity + OnlineBackup + DeviceProtection + TechSupport + Contract + PaperlessBilling + PaymentMethod + MonthlyCharges",
strata=strata_cols,
)
cph.plot_partial_effects_on_outcome(
"Contract",
values=["Month-to-month", "One year", "Two year"],
plot_baseline=False)
self.plt.title(
"test_coxph_plot_partial_effects_on_outcome_with_strata_and_complicated_dtypes"
)
self.plt.show(block=block)
def test_coxph_plot_partial_effects_on_outcome_with_multiple_variables_and_strata(self, block):
df = load_rossi()
df["strata"] = np.random.choice(["A", "B"], size=df.shape[0])
cp = CoxPHFitter()
cp.fit(df, "week", "arrest", strata="strata")
cp.plot_partial_effects_on_outcome(["age", "prio"], [[10, 0], [50, 10], [80, 90]])
self.plt.title("test_coxph_plot_partial_effects_on_outcome_with_multiple_variables_and_strata")
self.plt.show(block=block)
def test_coxph_plot_partial_effects_on_outcome_with_nonnumeric_strata(self, block):
df = load_rossi()
df["strata"] = np.random.choice(["A", "B"], size=df.shape[0])
cp = CoxPHFitter()
cp.fit(df, "week", "arrest", strata="strata")
cp.plot_partial_effects_on_outcome("age", [10, 50, 80])
self.plt.title("test_coxph_plot_partial_effects_on_outcome_with_single_strata")
self.plt.show(block=block)
def CoxRegressionModel(stimes, N, ap_s, ap_s2, ap_s3, ap_s5, ebb_, eap_, ebs,
aps_0, mu, stime, Np):
#ebs = singular value = 0, 0.175, 0.35, 0.525, 0.7
#stimes = 1050 values ( 5 eb values x 7 ap values x 30 survival times)
#stime = 30 values for each aps_) value
#N = 1050
#Np = 30
#ap_s, ap_s2, ap_s3 = array of 1050 values used for the data frame
#*************************************EVENT OBSERVATION**************************************************#
E = np.zeros(N).astype(int)
for i, time in enumerate(stimes):
if (time > 62831):
E[i] = 0
else:
E[i] = 1
#**************************************MAKING A DATA FRAME************************************************#
data1 = {
'T': stimes,
'E': E,
'aps': ap_s,
'aps2': ap_s2,
'aps3': ap_s3,
'aps5': ap_s5,
'eap': eap_,
'eb': ebb_
}
df = pd.DataFrame(data=data1)
T = df['T']
E = df['E']
aps = df['aps']
aps2 = df['aps2']
aps3 = df['aps3']
aps5 = df['aps5']
eap = df['eap']
eb = df['eb']
#print(df)
#************************************COX PH FITTER*************************************#
fig, axes = plt.subplots()
axes.set_xscale('log')
axes.set_ylabel("S(t)")
KT, KE, Kdf = PlottingLL.PlottingLL(ebs, aps_0, mu, stime, Np)
kmf = KaplanMeierFitter().fit(KT, KE, label='KaplanMeierFitter')
kmf.plot_survival_function(ax=axes)
cph = CoxPHFitter()
#cph.fit(df,duration_col = 'T', event_col = 'E', formula = "eap")
#cph.fit(df,duration_col = 'T', event_col = 'E')
cph.fit(df, duration_col='T', event_col='E', formula="aps + I(aps**3)")
#cph.print_summary()
cph.plot_partial_effects_on_outcome(plot_baseline=False,
ax=axes,
cmap="coolwarm")
#cph.plot_partial_effects_on_outcome(covariates = ['aps'], values = [round(aps_0,3)], plot_baseline = False, ax = axes, cmap = "coolwarm")
cph.baseline_survival_.plot(ax=axes, ls=":", color=f"C{i}")
#cph.fit(df,duration_col = 'T', event_col = 'E', formula = "eb + aps + I(aps**3)")
#cph.print_summary()
#cph.plot_partial_effects_on_outcome(covariates = ['aps'], values = [round(aps_0,3)], ax = axes)
plt.title('Formula(aps vs. aps3 (1050 values)): (eb,ap,mu)={}'.format(
(round(ebs, 3), round(aps_0, 3), mu)),
fontsize=12)
