def _fit(
        self,
        durations,
        event_observed=None,
        timeline=None,
        entry=None,
        label="KM_estimate",
        alpha=None,
        ci_labels=None,
        weights=None,
    ):  # pylint: disable=too-many-arguments,too-many-locals
        """
        Parameters
        ----------
          durations: an array, list, pd.DataFrame or pd.Series
            length n -- duration subject was observed for
          event_observed: an array, list, pd.DataFrame, or pd.Series, optional
             True if the the death was observed, False if the event was lost (right-censored). Defaults all True if event_observed==None
          timeline: an array, list, pd.DataFrame, or pd.Series, optional
            return the best estimate at the values in timelines (postively increasing)
          entry: an array, list, pd.DataFrame, or pd.Series, optional
             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
             entered study when they were "born".
          label: string, optional
            a string to name the column of the estimate.
          alpha: float, optional
            the alpha value in the confidence intervals. Overrides the initializing alpha for this call to fit only.
          left_censorship: bool, optional (default=False)
            True if durations and event_observed refer to left censorship events. Default False
          ci_labels: tuple, optional
                add custom column names to the generated confidence intervals as a length-2 list: [<lower-bound name>, <upper-bound name>]. Default: <label>_lower_<1-alpha/2>
          weights: an array, list, pd.DataFrame, or pd.Series, optional
              if providing a weighted dataset. For example, instead
              of providing every subject as a single element of `durations` and `event_observed`, one could
              weigh subject differently.

        Returns
        -------
        self: KaplanMeierFitter
          self with new properties like ``survival_function_``, ``plot()``, ``median``

        """
        self._check_values(durations)
        if event_observed is not None:
            self._check_values(event_observed)

        self._label = label

        if weights is not None:
            weights = np.asarray(weights)
            if (weights.astype(int) != weights).any():
                warnings.warn(
                    """It looks like your weights are not integers, possibly propensity scores then?
  It's important to know that the naive variance estimates of the coefficients are biased. Instead use Monte Carlo to
  estimate the variances. See paper "Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis"
  or "Adjusted Kaplan-Meier estimator and log-rank test with inverse probability of treatment weighting for survival data."
                  """,
                    StatisticalWarning,
                )

        # if the user is interested in left-censorship, we return the cumulative_density_, no survival_function_,
        is_left_censoring = self._censoring_type == CensoringType.LEFT
        primary_estimate_name = "survival_function_" if not is_left_censoring else "cumulative_density_"
        secondary_estimate_name = "cumulative_density_" if not is_left_censoring else "survival_function_"

        self.durations, self.event_observed, self.timeline, self.entry, self.event_table = _preprocess_inputs(
            durations, event_observed, timeline, entry, weights
        )

        alpha = alpha if alpha else self.alpha
        log_estimate, cumulative_sq_ = _additive_estimate(
            self.event_table, self.timeline, self._additive_f, self._additive_var, is_left_censoring
        )

        if entry is not None:
            # a serious problem with KM is that when the sample size is small and there are too few early
            # truncation times, it may happen that is the number of patients at risk and the number of deaths is the same.
            # we adjust for this using the Breslow-Fleming-Harrington estimator
            n = self.event_table.shape[0]
            net_population = (self.event_table["entrance"] - self.event_table["removed"]).cumsum()
            if net_population.iloc[: int(n / 2)].min() == 0:
                ix = net_population.iloc[: int(n / 2)].idxmin()
                raise StatError(
                    """There are too few early truncation times and too many events. S(t)==0 for all t>%g. Recommend BreslowFlemingHarringtonFitter."""
                    % ix
                )

        # estimation
        setattr(self, primary_estimate_name, pd.DataFrame(np.exp(log_estimate), columns=[self._label]))
        setattr(self, secondary_estimate_name, pd.DataFrame(1 - np.exp(log_estimate), columns=[self._label]))

        self.__estimate = getattr(self, primary_estimate_name)
        self.confidence_interval_ = self._bounds(cumulative_sq_[:, None], alpha, ci_labels)
        self.median_ = median_survival_times(self.__estimate, left_censorship=is_left_censoring)
        self._cumulative_sq_ = cumulative_sq_

        setattr(self, "confidence_interval_" + primary_estimate_name, self.confidence_interval_)
        setattr(self, "confidence_interval_" + secondary_estimate_name, 1 - self.confidence_interval_)

        # estimation methods
        self._estimation_method = primary_estimate_name
        self._estimate_name = primary_estimate_name
        self._predict_label = label
        self._update_docstrings()

        return self
def _fit ( self , durations , event_observed = None , timeline = None , entry = None , label = "KM_estimate" , alpha = None , ci_labels = None , weights = None , ) : self . _check_values ( durations ) if event_observed is not None : self . _check_values ( event_observed ) self . _label = label if weights is not None : weights = np . asarray ( weights ) if ( weights . astype ( int ) != weights ) . any ( ) : warnings . warn ( """It looks like your weights are not integers, possibly propensity scores then?
  It's important to know that the naive variance estimates of the coefficients are biased. Instead use Monte Carlo to
  estimate the variances. See paper "Variance estimation when using inverse probability of treatment weighting (IPTW) with survival analysis"
  or "Adjusted Kaplan-Meier estimator and log-rank test with inverse probability of treatment weighting for survival data."
                  """ , StatisticalWarning , ) is_left_censoring = self . _censoring_type == CensoringType . LEFT primary_estimate_name = "survival_function_" if not is_left_censoring else "cumulative_density_" secondary_estimate_name = "cumulative_density_" if not is_left_censoring else "survival_function_" self . durations , self . event_observed , self . timeline , self . entry , self . event_table = _preprocess_inputs ( durations , event_observed , timeline , entry , weights ) alpha = alpha if alpha else self . alpha log_estimate , cumulative_sq_ = _additive_estimate ( self . event_table , self . timeline , self . _additive_f , self . _additive_var , is_left_censoring ) if entry is not None : n = self . event_table . shape [ 0 ] net_population = ( self . event_table [ "entrance" ] - self . event_table [ "removed" ] ) . cumsum ( ) if net_population . iloc [ : int ( n / 2 ) ] . min ( ) == 0 : ix = net_population . iloc [ : int ( n / 2 ) ] . idxmin ( ) raise StatError ( """There are too few early truncation times and too many events. S(t)==0 for all t>%g. Recommend BreslowFlemingHarringtonFitter.""" % ix ) setattr ( self , primary_estimate_name , pd . DataFrame ( np . exp ( log_estimate ) , columns = [ self . _label ] ) ) setattr ( self , secondary_estimate_name , pd . DataFrame ( 1 - np . exp ( log_estimate ) , columns = [ self . _label ] ) ) self . __estimate = getattr ( self , primary_estimate_name ) self . confidence_interval_ = self . _bounds ( cumulative_sq_ [ : , None ] , alpha , ci_labels ) self . median_ = median_survival_times ( self . __estimate , left_censorship = is_left_censoring ) self . _cumulative_sq_ = cumulative_sq_ setattr ( self , "confidence_interval_" + primary_estimate_name , self . confidence_interval_ ) setattr ( self , "confidence_interval_" + secondary_estimate_name , 1 - self . confidence_interval_ ) self . _estimation_method = primary_estimate_name self . _estimate_name = primary_estimate_name self . _predict_label = label self . _update_docstrings ( ) return self
Parameters
        ----------
          durations: an array, list, pd.DataFrame or pd.Series
            length n -- duration subject was observed for
          event_observed: an array, list, pd.DataFrame, or pd.Series, optional
             True if the the death was observed, False if the event was lost (right-censored). Defaults all True if event_observed==None
          timeline: an array, list, pd.DataFrame, or pd.Series, optional
            return the best estimate at the values in timelines (postively increasing)
          entry: an array, list, pd.DataFrame, or pd.Series, optional
             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
             entered study when they were "born".
          label: string, optional
            a string to name the column of the estimate.
          alpha: float, optional
            the alpha value in the confidence intervals. Overrides the initializing alpha for this call to fit only.
          left_censorship: bool, optional (default=False)
            True if durations and event_observed refer to left censorship events. Default False
          ci_labels: tuple, optional
                add custom column names to the generated confidence intervals as a length-2 list: [<lower-bound name>, <upper-bound name>]. Default: <label>_lower_<1-alpha/2>
          weights: an array, list, pd.DataFrame, or pd.Series, optional
              if providing a weighted dataset. For example, instead
              of providing every subject as a single element of `durations` and `event_observed`, one could
              weigh subject differently.

        Returns
        -------
        self: KaplanMeierFitter
          self with new properties like ``survival_function_``, ``plot()``, ``median``