def test_survival_events_from_table_with_ties():
T, C = np.array([1, 2, 3, 4, 4, 5]), np.array([1, 0, 1, 1, 1, 1])
d = utils.survival_table_from_events(T, C)
T_, C_, W_ = utils.survival_events_from_table(d[["censored", "observed"]])
npt.assert_array_equal([1, 2, 3, 4, 5], T_)
npt.assert_array_equal([1, 0, 1, 1, 1], C_)
npt.assert_array_equal([1, 1, 1, 2, 1], W_)
def test_survival_events_from_table_no_ties():
T, C = np.array([1, 2, 3, 4, 4, 5]), np.array([1, 0, 1, 1, 0, 1])
d = utils.survival_table_from_events(T, C)
T_, C_, W_ = utils.survival_events_from_table(d[["censored", "observed"]])
npt.assert_array_equal(T, T_)
npt.assert_array_equal(C, C_)
npt.assert_array_equal(W_, np.ones_like(T))
def test_qq_plot_with_weights_and_entry(self, block):
from lifelines.utils import survival_events_from_table
df = pd.DataFrame(index=[60, 171, 263, 427, 505, 639])
df["death"] = [1, 1, 1, 0, 1, 0]
df["censored"] = [0, 0, 0, 3, 0, 330]
T, E, W = survival_events_from_table(df, observed_deaths_col="death", censored_col="censored")
wf = WeibullFitter().fit(T, E, weights=W, entry=0.0001 * np.ones_like(T))
ax = qq_plot(wf)
self.plt.suptitle("test_qq_plot_with_weights_and_entry")
self.plt.show(block=block)
def plot3(df):
import sys
#get_ipython().system('{sys.executable} -m pip install lifelines')
#install pandas and matlab plot
import pandas as pd
import matplotlib.pyplot as plt
from lifelines import KaplanMeierFitter
# import os
# os.chdir("/Users/MDONEGAN/Downloads")
#survival= pd.read_csv("/Users/MDONEGAN/Downloads/Book2.csv", sep=',')
survival = df
from lifelines.statistics import pairwise_logrank_test
results = pairwise_logrank_test(survival['time'], survival['group'],
survival['event'])
results.print_summary()
#%%
# this util converts a table with "death" and "censored" (alive) into the lifelines format
from lifelines import KaplanMeierFitter
from lifelines.utils import survival_events_from_table
kmf = KaplanMeierFitter()
ax = plt.subplot(111)
#df = pd.read_csv('/Users/MDONEGAN/Downloads/counts.csv')
df = df.set_index('time')
T, E, W = survival_events_from_table(df,
observed_deaths_col='death',
censored_col='censored')
kmf.fit(T, E, weights=W)
kmf.plot(ax=ax, ci_show=True, marker='o')
plt.xlabel("days")
plt.ylabel("survival %")
plt.ylim(0.4, 1.05)
#%%
#trying to combine the grouping function and the events from table function
from lifelines import KaplanMeierFitter
from lifelines.utils import survival_events_from_table
kmf = KaplanMeierFitter()
ax = plt.subplot(111)
#df = pd.read_csv('/Users/MDONEGAN/Downloads/counts.csv')
df = df.set_index('time')
T, E, W = survival_events_from_table(df,
observed_deaths_col='death',
censored_col='censored')
print(E)
#group dataset by treatment and plot all groups (treatments) using kmf fit
for name, T_group, E_group, W_group in T, E, W.groupby('group'):
kmf.fit(grouped_survival['T'], grouped_survival['E'], label=name)
kmf.plot(ax=ax, ci_show=False, marker='o')
plt.xlabel("days")
plt.ylabel("survival %")
plt.ylim(0.4, 1.05)
return fig_to_uri(plt)
def test_survival_table_to_events():
T, C = np.array([1, 2, 3, 4, 4, 5]), np.array([1, 0, 1, 1, 1, 1])
d = utils.survival_table_from_events(T, C, np.zeros_like(T))
T_, C_ = utils.survival_events_from_table(d[['censored', 'observed']])
npt.assert_array_equal(T, T_)
npt.assert_array_equal(C, C_)
def test_survival_table_to_events():
T, C = np.array([1, 2, 3, 4, 4, 5]), np.array([1, 0, 1, 1, 1, 1])
d = utils.survival_table_from_events(T, C, np.zeros_like(T))
T_, C_ = utils.survival_events_from_table(d[['censored', 'observed']])
npt.assert_array_equal(T, T_)
npt.assert_array_equal(C, C_)
