# -*- coding: utf-8 -*-
# weibull aft
if __name__ == "__main__":
import pandas as pd
import time
from lifelines import WeibullAFTFitter
from lifelines.datasets import load_rossi
df = load_rossi()
df = pd.concat([df] * 1)
# df = df.reset_index()
# df['week'] = np.random.exponential(1, size=df.shape[0])
wp = WeibullAFTFitter()
start_time = time.time()
wp.fit(df, duration_col="week", event_col="arrest")
print("--- %s seconds ---" % (time.time() - start_time))
wp.print_summary()
import numpy as np
from scipy.stats import weibull_min
import pandas as pd
from lifelines import WeibullAFTFitter, CoxPHFitter
# This is an implementation of https://uwspace.uwaterloo.ca/bitstream/handle/10012/10265/Cook_Richard-10265.pdf
N = 50000
p = 0.5
bX = np.log(0.5)
bZ = np.log(4)
Z = np.random.binomial(1, p, size=N)
X = np.random.binomial(1, 0.5, size=N)
X_ = 20000 + 10 * np.random.randn(N)
W = weibull_min.rvs(1, scale=1, loc=0, size=N)
Y = bX * X + bZ * Z + np.log(W)
T = np.exp(Y)
#######################################
df = pd.DataFrame({"T": T, "x": X, "x_": X_})
wf = WeibullAFTFitter().fit(df, "T")
wf.print_summary(4)
cph = CoxPHFitter().fit(df, "T", show_progress=True, step_size=1.0)
cph.print_summary(4)
color="failure",
)
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))
import pandas as pd
from lifelines import WeibullAFTFitter, CoxPHFitter
# This is an implementation of https://uwspace.uwaterloo.ca/bitstream/handle/10012/10265/Cook_Richard-10265.pdf
N = 50000
p = 0.5
bX = np.log(0.5)
bZ = np.log(4)
Z = np.random.binomial(1, p, size=N)
X = np.random.binomial(1, 0.5, size=N)
X_ = 20000 + 10 * np.random.randn(N)
W = weibull_min.rvs(1, scale=1, loc=0, size=N)
Y = bX * X + bZ * Z + np.log(W)
T = np.exp(Y)
#######################################
df = pd.DataFrame({"T": T, "x": X, "x_": X_})
wf = WeibullAFTFitter().fit(df, "T")
wf.print_summary(4)
cph = CoxPHFitter().fit(df, "T", show_progress=True, step_size=1.0)
cph.print_summary(4)
