def test_weibull_aft_plotting(self, block):
df = load_regression_dataset()
aft = WeibullAFTFitter()
aft.fit(df, "T", "E")
aft.plot()
self.plt.tight_layout()
self.plt.title("test_weibull_aft_plotting")
self.plt.show(block=block)
def test_weibull_aft_plotting_with_subset_of_columns(self, block):
df = load_regression_dataset()
aft = WeibullAFTFitter()
aft.fit(df, "T", "E")
aft.plot(columns=["var1", "var2"])
self.plt.tight_layout()
self.plt.title("test_weibull_aft_plotting_with_subset_of_columns")
self.plt.show(block=block)
)
prebreakdown_merge_len_acc_1500_model_df_one_hot_no_censor = prebreakdown_merge_len_acc_1500_model_df_one_hot.query(
"failure==1")
aft = WeibullAFTFitter()
aft.fit(
prebreakdown_merge_len_acc_1500_model_df_one_hot,
duration_col="mainline_vol",
event_col="failure",
formula=
"ramp_metering+length_of_acceleration_lane+ffs_cap_df+number_of_mainline_lane_downstream+simple_merge",
)
aft.print_summary()
aft.plot()
aft.median_survival_time_
aft.mean_survival_time_
fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(5, 4))
aft.plot_partial_effects_on_outcome(
"ramp_metering",
[0, 1],
cmap="coolwarm",
ax=ax,
plot_baseline=False,
times=range(1000, 3200, 50),
)
fig2, ax1 = plt.subplots(nrows=1, ncols=1, figsize=(5, 4))
aft.plot_partial_effects_on_outcome(
