final clean up; tests pass

lifelines/_base_fitter.py

@@ -1,8 +1,10 @@
 # -*- coding: utf-8 -*-
 from __future__ import print_function
 import numpy as np
+import pandas as pd
 from lifelines.plotting import plot_estimate
-from lifelines.utils import _conditional_time_to_event_
+from lifelines.utils import qth_survival_times
+
 
 class BaseFitter(object):
 
@@ -20,6 +22,7 @@ def __repr__(self):
             s = """<lifelines.%s>""" % classname
         return s
 
+
 class UnivariateFitter(BaseFitter):
 
     def _plot_estimate(self, *args):
@@ -46,7 +49,6 @@ def subtract(other):
         subtract.__doc__ = doc_string
         return subtract
 
-
     def _divide(self, estimate):
         class_name = self.__class__.__name__
         doc_string = """
@@ -68,7 +70,6 @@ def divide(other):
         divide.__doc__ = doc_string
         return divide
 
-
     def _predict(self, estimate, label):
         class_name = self.__class__.__name__
         doc_string = """
@@ -93,4 +94,21 @@ def predict(time):
 
     @property
     def conditional_time_to_event_(self):
-        return _conditional_time_to_event_(self)
+        return self._conditional_time_to_event_()
+
+    def _conditional_time_to_event_(self):
+        """
+        Return a DataFrame, with index equal to survival_function_, that estimates the median
+        duration remaining until the death event, given survival up until time t. For example, if an
+        individual exists until age 1, their expected life remaining *given they lived to time 1*
+        might be 9 years.
+
+        Returns:
+            conditional_time_to_: DataFrame, with index equal to survival_function_
+
+        """
+        age = self.survival_function_.index.values[:, None]
+        columns = ['%s - Conditional time remaining to event' % self._label]
+        return pd.DataFrame(qth_survival_times(self.survival_function_[self._label] * 0.5, self.survival_function_).T.sort(ascending=False).values,
+                            index=self.survival_function_.index,
+                            columns=columns) - age

lifelines/aalen_additive_fitter.py

@@ -2,13 +2,11 @@
 from __future__ import print_function
 import numpy as np
 import pandas as pd
-
-from numpy import dot, exp
 from numpy.linalg import LinAlgError
 from scipy.integrate import trapz
 from lifelines._base_fitter import BaseFitter
 from lifelines.utils import _get_index, inv_normal_cdf, epanechnikov_kernel, \
-         ridge_regression as lr, qth_survival_times
+    ridge_regression as lr, qth_survival_times
 from lifelines.progress_bar import progress_bar
 from lifelines.plotting import plot_regressions
 
@@ -410,4 +408,3 @@ def predict_expectation(self, X):
 
     def predict(self, X):
         return self.predict_median(X)
-

lifelines/breslow_fleming_harrington_fitter.py

@@ -1,12 +1,10 @@
 # -*- coding: utf-8 -*-
 from __future__ import print_function
 import numpy as np
-import pandas as pd
 
 from lifelines._base_fitter import UnivariateFitter
 from lifelines.nelson_aalen_fitter import NelsonAalenFitter
-from lifelines.utils import preprocess_inputs, _additive_estimate, median_survival_times,\
-    inv_normal_cdf
+from lifelines.utils import median_survival_times
 
 
 class BreslowFlemingHarringtonFitter(UnivariateFitter):
@@ -69,4 +67,3 @@ def fit(self, durations, event_observed=None, timeline=None, entry=None,
         self.plot = self._plot_estimate("survival_function_")
         self.plot_survival_function = self.plot
         return self
-

lifelines/coxph_fitter.py

@@ -10,7 +10,7 @@
 
 from lifelines._base_fitter import BaseFitter
 from lifelines.utils import survival_table_from_events, inv_normal_cdf, normalize,\
-        significance_code, concordance_index, _get_index, qth_survival_times
+    significance_code, concordance_index, _get_index, qth_survival_times
 
 
 class CoxPHFitter(BaseFitter):

lifelines/estimation.py

@@ -1,15 +1,9 @@
 # -*- coding: utf-8 -*-
-from __future__ import print_function
-
-import numpy as np
-import pandas as pd
-
 from lifelines._base_fitter import BaseFitter
 from lifelines.weibull_fitter import WeibullFitter
 from lifelines.exponential_fitter import ExponentialFitter
 from lifelines.nelson_aalen_fitter import NelsonAalenFitter
 from lifelines.kaplan_meier_fitter import KaplanMeierFitter
 from lifelines.breslow_fleming_harrington_fitter import BreslowFlemingHarringtonFitter
-from lifelines.coxph_fitter import CoxPHFitter 
+from lifelines.coxph_fitter import CoxPHFitter
 from lifelines.aalen_additive_fitter import AalenAdditiveFitter
-

lifelines/exponential_fitter.py

@@ -6,6 +6,7 @@
 from lifelines._base_fitter import UnivariateFitter
 from lifelines.utils import inv_normal_cdf
 
+
 class ExponentialFitter(UnivariateFitter):
 
     """

lifelines/kaplan_meier_fitter.py

@@ -4,8 +4,8 @@
 import pandas as pd
 
 from lifelines._base_fitter import UnivariateFitter
-from lifelines.utils import preprocess_inputs, _additive_estimate, StatError, inv_normal_cdf,\
-        median_survival_times
+from lifelines.utils import _preprocess_inputs, _additive_estimate, StatError, inv_normal_cdf,\
+    median_survival_times
 
 
 class KaplanMeierFitter(UnivariateFitter):
@@ -28,7 +28,7 @@ def fit(self, durations, event_observed=None, timeline=None, entry=None, label='
           event_observed: an array, or pd.Series, of length n -- True if the the death was observed, False if the event
              was lost (right-censored). Defaults all True if event_observed==None
           entry: an array, or pd.Series, of length n -- relative time when a subject entered the study. This is
-             useful for left-truncated (not left-censored) observations. If None, all members of the population 
+             useful for left-truncated (not left-censored) observations. If None, all members of the population
              were born at time 0.
           label: a string to name the column of the estimate.
           alpha: the alpha value in the confidence intervals. Overrides the initializing
@@ -44,7 +44,7 @@ def fit(self, durations, event_observed=None, timeline=None, entry=None, label='
         """
         # if the user is interested in left-censorship, we return the cumulative_density_, no survival_function_,
         estimate_name = 'survival_function_' if not left_censorship else 'cumulative_density_'
-        v = preprocess_inputs(durations, event_observed, timeline, entry)
+        v = _preprocess_inputs(durations, event_observed, timeline, entry)
         self.durations, self.event_observed, self.timeline, self.entry, self.event_table = v
         self._label = label
         alpha = alpha if alpha else self.alpha
@@ -98,4 +98,4 @@ def _additive_f(self, population, deaths):
 
     def _additive_var(self, population, deaths):
         np.seterr(divide='ignore')
-        return (1. * deaths / (population * (population - deaths))).replace([np.inf], 0)
+        return (1. * deaths / (population * (population - deaths))).replace([np.inf], 0)

lifelines/nelson_aalen_fitter.py

@@ -4,8 +4,9 @@
 import pandas as pd
 
 from lifelines._base_fitter import UnivariateFitter
-from lifelines.utils import preprocess_inputs, _additive_estimate, epanechnikov_kernel,\
-        inv_normal_cdf
+from lifelines.utils import _preprocess_inputs, _additive_estimate, epanechnikov_kernel,\
+    inv_normal_cdf
+
 
 class NelsonAalenFitter(UnivariateFitter):
 
@@ -55,7 +56,7 @@ def fit(self, durations, event_observed=None, timeline=None, entry=None,
 
         """
 
-        v = preprocess_inputs(durations, event_observed, timeline, entry)
+        v = _preprocess_inputs(durations, event_observed, timeline, entry)
         self.durations, self.event_observed, self.timeline, self.entry, self.event_table = v
 
         cumulative_hazard_, cumulative_sq_ = _additive_estimate(self.event_table, self.timeline,

lifelines/plotting.py

@@ -330,7 +330,7 @@ def plot(ix=None, iloc=None, flat=False, show_censors=False,
             estimate_ = cls.smoothed_hazard_(bandwidth)
             confidence_interval_ = \
                 cls.smoothed_hazard_confidence_intervals_(bandwidth,
-                                                           hazard_=estimate_.values[:, 0])
+                                                          hazard_=estimate_.values[:, 0])
         else:
             confidence_interval_ = getattr(cls, 'confidence_interval_')
             estimate_ = getattr(cls, estimate)