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(