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fix test
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@CamDavidsonPilon
CamDavidsonPilon committed May 16, 2020
1 parent 8578b56 commit 94b2193
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Showing 4 changed files with 57 additions and 32 deletions.
3 changes: 2 additions & 1 deletion lifelines/fitters/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -494,7 +494,8 @@ def _fit_model(self, Ts, E, entry, weights, show_progress=True):

minimizing_results, previous_results, minimizing_ll = None, None, np.inf
for method, option in zip(
["Nelder-Mead", self._scipy_fit_method], [{"maxiter": 25}, {**{"disp": show_progress}, **self._scipy_fit_options}]
["Nelder-Mead", self._scipy_fit_method],
[{"maxiter": 100}, {**{"disp": show_progress}, **self._scipy_fit_options}],
):

initial_value = self._initial_values if previous_results is None else utils._to_1d_array(previous_results.x)
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2 changes: 1 addition & 1 deletion lifelines/fitters/generalized_gamma_fitter.py
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Original file line number Diff line number Diff line change
Expand Up @@ -115,7 +115,7 @@ def _create_initial_point(self, Ts, E, *args):
log_data = log(Ts[1])
elif CensoringType.is_interval_censoring(self):
# this fails if Ts[1] == Ts[0], so we add a some fudge factors.
log_data = log(Ts[1] - Ts[0] + 0.01)
log_data = log(Ts[1] - Ts[0] + 0.1)
return np.array([log_data.mean(), log(log_data.std() + 0.01), 0.1])

def _cumulative_hazard(self, params, times):
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80 changes: 52 additions & 28 deletions lifelines/tests/test_estimation.py
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Original file line number Diff line number Diff line change
Expand Up @@ -260,6 +260,9 @@ def test_models_can_handle_really_small_duration_values_for_left_censorship(self
T = np.maximum(T1, T2)

for fitter in known_parametric_univariate_fitters:
if isinstance(fitter(), PiecewiseExponentialFitterTesting):
# not a good model since the "break" is at 5.
continue
fitter().fit_left_censoring(T, E)

def test_parametric_univariate_fitters_can_print_summary(
Expand Down Expand Up @@ -383,7 +386,8 @@ def test_default_alpha_is_005(self, univariate_fitters):
assert f().alpha == 0.05

def test_univariate_fitters_accept_late_entries(self, positive_sample_lifetimes, univariate_fitters):
entries = 0.1 * positive_sample_lifetimes[0]
positive_sample_lifetimes = positive_sample_lifetimes
entries = positive_sample_lifetimes[0] - 3
for fitter in univariate_fitters:
f = fitter().fit(positive_sample_lifetimes[0], entry=entries)
assert f.entry is not None
Expand Down Expand Up @@ -3124,26 +3128,38 @@ def test_print_summary(self, rossi, cph):
repr(cph)
+ "\n"
+ """
duration col = week
event col = arrest
number of subjects = 432
number of events = 114
log-likelihood = -658.748
time fit was run = 2018-10-23 02:40:45 UTC
<lifelines.CoxPHFitter: fitted with 432 total observations, 318 right-censored observations>
duration col = 'week'
event col = 'arrest'
baseline estimation = breslow
number of observations = 432
number of events observed = 114
partial log-likelihood = -658.75
time fit was run = 2018-10-23 02:40:45 UTC
---
coef exp(coef) se(coef) z p coef lower 95% coef upper 95%
fin -0.3794 0.6843 0.1914 -1.9826 0.0474 -0.7545 -0.0043
age -0.0574 0.9442 0.0220 -2.6109 0.0090 -0.1006 -0.0143
race 0.3139 1.3688 0.3080 1.0192 0.3081 -0.2898 0.9176
wexp -0.1498 0.8609 0.2122 -0.7058 0.4803 -0.5657 0.2662
mar -0.4337 0.6481 0.3819 -1.1358 0.2561 -1.1821 0.3147
paro -0.0849 0.9186 0.1958 -0.4336 0.6646 -0.4685 0.2988
prio 0.0915 1.0958 0.0286 3.1939 0.0014 0.0353 0.1476
coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%
fin -0.38 0.68 0.19 -0.75 -0.00 0.47 1.00
age -0.06 0.94 0.02 -0.10 -0.01 0.90 0.99
race 0.31 1.37 0.31 -0.29 0.92 0.75 2.50
wexp -0.15 0.86 0.21 -0.57 0.27 0.57 1.30
mar -0.43 0.65 0.38 -1.18 0.31 0.31 1.37
paro -0.08 0.92 0.20 -0.47 0.30 0.63 1.35
prio 0.09 1.10 0.03 0.04 0.15 1.04 1.16
z p -log2(p)
fin -1.98 0.05 4.40
age -2.61 0.01 6.79
race 1.02 0.31 1.70
wexp -0.71 0.48 1.06
mar -1.14 0.26 1.97
paro -0.43 0.66 0.59
prio 3.19 <0.005 9.48
---
Concordance = 0.640
Log-likelihood ratio test = 33.27 on 7 df, -log2(p)=15.37
Concordance = 0.64
Partial AIC = 1331.50
log-likelihood ratio test = 33.27 on 7 df
-log2(p) of ll-ratio test = 15.37
"""
)
.strip()
Expand Down Expand Up @@ -4949,22 +4965,30 @@ def test_print_summary(self, ctv, heart):
repr(ctv)
+ "\n"
+ """
event col = event
<lifelines.CoxTimeVaryingFitter: fitted with 172 periods, 103 subjects, 75 events>
event col = 'event'
number of subjects = 103
number of periods = 172
number of events = 75
log-likelihood = -290.566
partial log-likelihood = -290.57
time fit was run = 2018-10-23 02:41:45 UTC
---
coef exp(coef) se(coef) z p coef lower 95% coef upper 95%
age 0.0272 1.0275 0.0137 1.9809 0.0476 0.0003 0.0540
year -0.1463 0.8639 0.0705 -2.0768 0.0378 -0.2845 -0.0082
surgery -0.6372 0.5288 0.3672 -1.7352 0.0827 -1.3570 0.0825
transplant -0.0103 0.9898 0.3138 -0.0327 0.9739 -0.6252 0.6047
coef exp(coef) se(coef) coef lower 95% coef upper 95% exp(coef) lower 95% exp(coef) upper 95%
age 0.03 1.03 0.01 0.00 0.05 1.00 1.06
year -0.15 0.86 0.07 -0.28 -0.01 0.75 0.99
surgery -0.64 0.53 0.37 -1.36 0.08 0.26 1.09
transplant -0.01 0.99 0.31 -0.63 0.60 0.54 1.83
z p -log2(p)
age 1.98 0.05 4.39
year -2.08 0.04 4.72
surgery -1.74 0.08 3.60
transplant -0.03 0.97 0.04
---
Likelihood ratio test = 15.11 on 4 df, -log2(p)=7.80
Partial AIC = 589.13
log-likelihood ratio test = 15.11 on 4 df
-log2(p) of ll-ratio test = 7.80
"""
)
.strip()
Expand Down Expand Up @@ -5169,4 +5193,4 @@ def _create_initial_point(self, Ts, E, entry, weights):
wmc.fit(T, E)
mcfitter.fit(T, E)

assert abs(wmc.c_ - mcfitter.cured_fraction_) < 0.001
assert_frame_equal(wmc.summary.reset_index(drop=True), mcfitter.summary.reset_index(drop=True), check_less_precise=0)
4 changes: 2 additions & 2 deletions lifelines/tests/test_plotting.py
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Original file line number Diff line number Diff line change
Expand Up @@ -218,8 +218,8 @@ def test_aalen_additive_plot(self, block):

def test_kmf_with_interval_censoring_plotting(self, block):
kmf = KaplanMeierFitter()
left, right = load_diabetes()[["left", "right"]]
kmf.fit(left, right)
left, right = load_diabetes()["left"], load_diabetes()["right"]
kmf.fit_interval_censoring(left, right)
kmf.plot(color="r")
self.plt.show(block=block)
return
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