def test_plot_lifetimes_relative(self, block):
self.plt.figure()
t = np.linspace(0, 20, 1000)
hz, coef, covrt = generate_hazard_rates(1, 5, t)
N = 20
T, C = generate_random_lifetimes(hz, t, size=N, censor=True)
plot_lifetimes(T, event_observed=C, block=block)
def test_aalen_additive_fit_with_censor(self):
# this is a visual test of the fitting the cumulative
# hazards.
matplotlib = pytest.importorskip("matplotlib")
from matplotlib import pyplot as plt
n = 2500
d = 6
timeline = np.linspace(0, 70, 10000)
hz, coef, X = generate_hazard_rates(n, d, timeline)
X.columns = coef.columns
cumulative_hazards = pd.DataFrame(cumulative_integral(coef.values, timeline),
index=timeline, columns=coef.columns)
T = generate_random_lifetimes(hz, timeline)
X['T'] = T
X['E'] = np.random.binomial(1, 0.99, n)
aaf = AalenAdditiveFitter()
aaf.fit(X, 'T', 'E')
for i in range(d + 1):
ax = plt.subplot(d + 1, 1, i + 1)
col = cumulative_hazards.columns[i]
ax = cumulative_hazards[col].ix[:15].plot(legend=False, ax=ax)
ax = aaf.plot(ix=slice(0, 15), ax=ax, columns=[col], legend=False)
plt.show()
def test_plot_lifetimes_calendar(self, block):
self.plt.figure()
t = np.linspace(0, 20, 1000)
hz, coef, covrt = generate_hazard_rates(1, 5, t)
N = 20
current = 10
birthtimes = current * np.random.uniform(size=(N,))
T, C = generate_random_lifetimes(hz, t, size=N, censor=current - birthtimes)
plot_lifetimes(T, event_observed=C, birthtimes=birthtimes, block=block)
def test_aalen_additive_median_predictions_split_data(self):
# This tests to make sure that my median predictions statisfy
# the prediction are greater than the actual 1/2 the time.
# generate some hazard rates and a survival data set
n = 2500
d = 5
timeline = np.linspace(0, 70, 5000)
hz, coef, X = generate_hazard_rates(n, d, timeline)
T = generate_random_lifetimes(hz, timeline)
X['T'] = T
# fit it to Aalen's model
aaf = AalenAdditiveFitter()
aaf.fit(X, 'T')
# predictions
T_pred = aaf.predict_median(X[list(range(6))])
assert abs((T_pred.values > T).mean() - 0.5) < 0.05
def test_aalen_additive_smoothed_plot(self, block):
# this is a visual test of the fitting the cumulative
# hazards.
n = 2500
d = 3
timeline = np.linspace(0, 150, 5000)
hz, coef, X = generate_hazard_rates(n, d, timeline)
T = generate_random_lifetimes(hz, timeline) + 0.1 * np.random.uniform(size=(n, 1))
C = np.random.binomial(1, 0.8, size=n)
X['T'] = T
X['E'] = C
# fit the aaf, no intercept as it is already built into X, X[2] is ones
aaf = AalenAdditiveFitter(coef_penalizer=0.1, fit_intercept=False)
aaf.fit(X, 'T', 'E')
ax = aaf.smoothed_hazards_(1).iloc[0:aaf.cumulative_hazards_.shape[0] - 500].plot()
ax.set_xlabel("time")
ax.set_title('test_aalen_additive_smoothed_plot')
self.plt.show(block=block)
return
def test_aalen_additive_plot(self):
# this is a visual test of the fitting the cumulative
# hazards.
n = 2500
d = 3
timeline = np.linspace(0, 70, 10000)
hz, coef, X = generate_hazard_rates(n, d, timeline)
T = generate_random_lifetimes(hz, timeline)
C = np.random.binomial(1, 1., size=n)
X['T'] = T
X['E'] = C
# fit the aaf, no intercept as it is already built into X, X[2] is ones
aaf = AalenAdditiveFitter(coef_penalizer=0.1, fit_intercept=False)
aaf.fit(X, 'T', 'E')
ax = aaf.plot(iloc=slice(0, aaf.cumulative_hazards_.shape[0] - 100))
ax.set_xlabel("time")
ax.set_title('test_aalen_additive_plot')
self.plt.show()
return
def test_aalen_additive_smoothed_plot(self, block):
# this is a visual test of the fitting the cumulative
# hazards.
n = 2500
d = 3
timeline = np.linspace(0, 150, 5000)
hz, coef, X = generate_hazard_rates(n, d, timeline)
T = generate_random_lifetimes(
hz, timeline) + 0.1 * np.random.uniform(size=(n, 1))
C = np.random.binomial(1, 0.8, size=n)
X["T"] = T
X["E"] = C
# fit the aaf, no intercept as it is already built into X, X[2] is ones
aaf = AalenAdditiveFitter(coef_penalizer=0.1, fit_intercept=False)
aaf.fit(X, "T", "E")
ax = aaf.smoothed_hazards_(1).iloc[0:aaf.cumulative_hazards_.shape[0] -
500].plot()
ax.set_xlabel("time")
ax.set_title("test_aalen_additive_smoothed_plot")
self.plt.show(block=block)
return
def test_aalen_additive_plot(self, block):
# this is a visual test of the fitting the cumulative
# hazards.
n = 2500
d = 3
timeline = np.linspace(0, 70, 10000)
hz, coef, X = generate_hazard_rates(n, d, timeline)
T = generate_random_lifetimes(hz, timeline)
T[np.isinf(T)] = 10
C = np.random.binomial(1, 1.0, size=n)
X["T"] = T
X["E"] = C
# fit the aaf, no intercept as it is already built into X, X[2] is ones
aaf = AalenAdditiveFitter(coef_penalizer=0.1, fit_intercept=False)
aaf.fit(X, "T", "E")
ax = aaf.plot(iloc=slice(0, aaf.cumulative_hazards_.shape[0] - 100))
ax.set_xlabel("time")
ax.set_title("test_aalen_additive_plot")
self.plt.show(block=block)
return
def test_aalen_additive_fit_no_censor(self, block):
n = 2500
d = 6
timeline = np.linspace(0, 70, 10000)
hz, coef, X = generate_hazard_rates(n, d, timeline)
X.columns = coef.columns
cumulative_hazards = pd.DataFrame(cumulative_integral(coef.values, timeline),
index=timeline, columns=coef.columns)
T = generate_random_lifetimes(hz, timeline)
X['T'] = T
X['E'] = np.random.binomial(1, 1, n)
aaf = AalenAdditiveFitter()
aaf.fit(X, 'T', 'E')
for i in range(d + 1):
ax = self.plt.subplot(d + 1, 1, i + 1)
col = cumulative_hazards.columns[i]
ax = cumulative_hazards[col].loc[:15].plot(legend=False, ax=ax)
ax = aaf.plot(loc=slice(0, 15), ax=ax, columns=[col], legend=False)
self.plt.title("test_aalen_additive_fit_no_censor")
self.plt.show(block=block)
return
