CHANGELOG.md

Changelogs

0.14.0

• adding plot_covariate_groups to CoxPHFitter to visualize what happens to survival as we vary a covariate, all else being equal.
• utils functions like qth_survival_times and median_survival_times now return the transpose of the DataFrame compared to previous version of lifelines. The reason for this is that we often treat survival curves as columns in DataFrames, and functions of the survival curve as index (ex: KaplanMeierFitter.survival_function_ returns a survival curve at time t).
• KaplanMeierFitter.fit and NelsonAalenFitter.fit accept a weights vector that can be used for pre-aggregated datasets. See this issue.
• Convergence errors now return a custom ConvergenceWarning instead of a RuntimeWarning
• New checks for complete separation in the dataset for regressions.

0.13.0

• removes is_significant and test_result from StatisticalResult. Users can instead choose their significance level by comparing to p_value. The string representation of this class has changed aswell.
• CoxPHFitter and AalenAdditiveFitter now have a score_ property that is the concordance-index of the dataset to the fitted model.
• CoxPHFitter and AalenAdditiveFitter no longer have the data property. It was an almost duplicate of the training data, but was causing the model to be very large when serialized.
• Implements a new fitter CoxTimeVaryingFitter available under the lifelines namespace. This model implements the Cox model for time-varying covariates.
• Utils for creating time varying datasets available in utils.
• less noisy check for complete separation.
• removed datasets namespace from the main lifelines namespace
• CoxPHFitter has a slightly more intelligent (barely...) way to pick a step size, so convergence should generally be faster.
• CoxPHFitter.fit now has accepts a weight_col kwarg so one can pass in weights per observation. This is very useful if you have many subjects, and the space of covariates is not large. Thus you can group the same subjects together and give that observation a weight equal to the count. Altogether, this means a much faster regression.

0.12.0

• removes include_likelihood from CoxPHFitter.fit - it was not slowing things down much (empirically), and often I wanted it for debugging (I suppose others do too). It's also another exit condition, so we many exit from the NR iterations faster.
• added step_size param to CoxPHFitter.fit - the default is good, but for extremely large or small datasets this may want to be set manually.
• added a warning to CoxPHFitter to check for complete seperation: https://stats.idre.ucla.edu/other/mult-pkg/faq/general/faqwhat-is-complete-or-quasi-complete-separation-in-logisticprobit-regression-and-how-do-we-deal-with-them/
• Additional functionality to utils.survival_table_from_events to bin the index to make the resulting table more readable.

0.11.3

• No longer support matplotlib 1.X
• Adding times argument to CoxPHFitter's predict_survival_function and predict_cumulative_hazard to predict the estimates at, instead uses the default times of observation or censorship.
• More accurate prediction methods parametrics univariate models.

0.11.2

• Changing liscense to valilla MIT.
• Speed up NelsonAalenFitter.fit considerably.

0.11.1

• Python3 fix for CoxPHFitter.plot.

0.11.0

• fixes regression in KaplanMeierFitter.plot when using Seaborn and lifelines.
• introduce a new .plot function to a fitted CoxPHFitter instance. This plots the hazard coefficients and their confidence intervals.
• in all plot methods, the ix kwarg has been deprecated in favour of a new loc kwarg. This is to align with Pandas deprecating ix

0.10.1

• fix in internal normalization for CoxPHFitter predict methods.

0.10.0

• corrected bug that was returning the wrong baseline survival and hazard values in CoxPHFitter when normalize=True.
• removed normalize kwarg in CoxPHFitter. This was causing lots of confusion for users, and added code complexity. It's really nice to be able to remove it.
• correcting column name in CoxPHFitter.baseline_survival_
• CoxPHFitter.baseline_cumulative_hazard_ is always centered, to mimic R's basehaz API.
• new predict_log_partial_hazards to CoxPHFitter

0.9.4

• adding plot_loglogs to KaplanMeierFitter
• added a (correct) check to see if some columns in a dataset will cause convergence problems.
• removing flat argument in plot methods. It was causing confusion. To replicate it, one can set ci_force_lines=True and show_censors=True.
• adding strata keyword argument to CoxPHFitter on initialization (ex: CoxPHFitter(strata=['v1', 'v2']). Why? Fitters initialized with strata can now be passed into k_fold_cross_validation, plus it makes unit testing strata fitters easier.
• If using strata in CoxPHFitter, access to strata specific baseline hazards and survival functions are available (previously it was a blended valie). Prediction also uses the specific baseline hazards/survivals.
• performance improvements in CoxPHFitter - should see at least a 10% speed improvement in fit.

0.9.2

• deprecates Pandas versions before 0.18.
• throw an error if no admissable pairs in the c-index calculation. Previously a NaN was returned.

0.9.1

• add two summary functions to Weibull and Exponential fitter, solves #224

0.9.0

• new prediction function in CoxPHFitter, predict_log_hazard_relative_to_mean, that mimics what R's predict.coxph does.
• removing the predict method in CoxPHFitter and AalenAdditiveFitter. This is because the choice of predict_median as a default was causing too much confusion, and no other natual choice as a default was available. All other predict_ methods remain.
• Default predict method in k_fold_cross_validation is now predict_expectation

0.8.1

• supports matplotlib 1.5.
• introduction of a param nn_cumulative_hazards in AalenAdditiveModel's __init__ (default True). This parameter will truncate all non-negative cumulative hazards in prediction methods to 0.
• bug fixes including:
  • fixed issue where the while loop in _newton_rhaphson would break too early causing a variable not to be set properly.
  • scaling of smooth hazards in NelsonAalenFitter was off by a factor of 0.5.

0.8.0

• reorganized lifelines directories:
  • moved test files out of main directory.
  • moved utils.py into it's own directory.
  • moved all estimators fitters directory.
• added a at_risk column to the output of group_survival_table_from_events and survival_table_from_events
• added sample size and power calculations for statistical tests. See lifeline.statistics. sample_size_necessary_under_cph and lifelines.statistics. power_under_cph.
• fixed a bug when using KaplanMeierFitter for left-censored data.

0.7.1

• addition of a l2 penalizer to CoxPHFitter.
• dropped Fortran implementation of efficient Python version. Lifelines is pure python once again!
• addition of strata keyword argument to CoxPHFitter to allow for stratification of a single or set of categorical variables in your dataset.
• datetimes_to_durations now accepts a list as na_values, so multiple values can be checked.
• fixed a bug in datetimes_to_durations where fill_date was not properly being applied.
• Changed warning in datetimes_to_durations to be correct.
• refactor each fitter into it's own submodule. For now, the tests are still in the same file. This will also not break the API.

0.7.0

• allow for multiple fitters to be passed into k_fold_cross_validation.
• statistical tests in lifelines.statistics. now return a StatisticalResult object with properties like p_value, test_results, and summary.
• fixed a bug in how log-rank statistical tests are performed. The covariance matrix was not being correctly calculated. This resulted in slightly different p-values.
• WeibullFitter, ExponentialFitter, KaplanMeierFitter and BreslowFlemingHarringtonFitter all have a conditional_time_to_event_ property that measures the median duration remaining until the death event, given survival up until time t.

0.6.1

• addition of median_ property to WeibullFitter and ExponentialFitter.
• WeibullFitter and ExponentialFitter will use integer timelines instead of float provided by linspace. This is so if your work is to sum up the survival function (for expected values or something similar), it's more difficult to make a mistake.

0.6.0

• Inclusion of the univariate fitters WeibullFitter and ExponentialFitter.
• Removing BayesianFitter from lifelines.
• Added new penalization scheme to AalenAdditiveFitter. You can now add a smoothing penalizer that will try to keep subsequent values of a hazard curve close together. The penalizing coefficient is smoothing_penalizer.
• Changed penalizer keyword arg to coef_penalizer in AalenAdditiveFitter.
• new ridge_regression function in utils.py to perform linear regression with l2 penalizer terms.
• Matplotlib is no longer a mandatory dependency.
• .predict(time) method on univariate fitters can now accept a scalar (and returns a scalar) and an iterable (and returns a numpy array)
• In KaplanMeierFitter, epsilon has been renamed to precision.

0.5.1

• New API for CoxPHFitter and AalenAdditiveFitter: the default arguments for event_col and duration_col. duration_col is now mandatory, and event_col now accepts a column, or by default, None, which assumes all events are observed (non-censored).
• Fix statistical tests.
• Allow negative durations in Fitters.
• New API in survival_table_from_events: min_observations is replaced by birth_times (default None).
• New API in CoxPHFitter for summary: summary will return a dataframe with statistics, print_summary() will print the dataframe (plus some other statistics) in a pretty manner.
• Adding "At Risk" counts option to univariate fitter plot methods, .plot(at_risk_counts=True), and the function lifelines.plotting.add_at_risk_counts.
• Fix bug Epanechnikov kernel.

0.5.0

• move testing to py.test
• refactor tests into smaller files
• make test_pairwise_logrank_test_with_identical_data_returns_inconclusive a better test
• add test for summary()
• Alternate metrics can be used for k_fold_cross_validation.

0.4.4

• Lots of improvements to numerical stability (but something things still need work)
• Additions to summary in CoxPHFitter.
• Make all prediction methods output a DataFrame
• Fixes bug in 1-d input not returning in CoxPHFitter
• Lots of new tests.

####0.4.3

• refactoring of qth_survival_times: it can now accept an iterable (or a scalar still) of probabilities in the q argument, and will return a DataFrame with these as columns. If len(q)==1 and a single survival function is given, will return a scalar, not a DataFrame. Also some good speed improvements.
• KaplanMeierFitter and NelsonAalenFitter now have a _label property that is passed in during the fit.
• KaplanMeierFitter/NelsonAalenFitter's inital alpha value is overwritten if a new alpha value is passed in during the fit.
• New method for KaplanMeierFitter: conditional_time_to. This returns a DataFrame of the estimate: med(S(t | T>s)) - s, human readable: the estimated time left of living, given an individual is aged s.
• Adds option include_likelihood to CoxPHFitter fit method to save the final log-likelihood value.

####0.4.2

• Massive speed improvements to CoxPHFitter.
• Additional prediction method: predict_percentile is available on CoxPHFitter and AalenAdditiveFitter. Given a percentile, p, this function returns the value t such that S(t | x) = p. It is a generalization of predict_median.
• Additional kwargs in k_fold_cross_validation that will accept different prediction methods (default is predict_median).
• Bug fix in CoxPHFitter predict_expectation function.
• Correct spelling mistake in newton-rhapson algorithm.
• datasets now contains functions for generating the respective datasets, ex: generate_waltons_dataset.
• Bumping up the number of samples in statistical tests to prevent them from failing so often (this a stop-gap)
• pep8 everything

####0.4.1.1

• Ability to specify default printing in statsitical tests with the suppress_print keyword argument (default False).
• For the multivariate log rank test, the inverse step has been replaced with the generalized inverse. This seems to be what other packages use.
• Adding more robust cross validation scheme based on issue #67.
• fixing regression_dataset in datasets.

####0.4.1

• CoxFitter is now known as CoxPHFitter
• refactoring some tests that used redundant data from lifelines.datasets.
• Adding cross validation: in utils is a new k_fold_cross_validation for model selection in regression problems.
• Change CoxPHFitter's fit method's display_output to False.
• fixing bug in CoxPHFitter's _compute_baseline_hazard that errored when sending Series objects to survival_table_from_events.
• CoxPHFitter's fit now looks to columns with too low variance, and halts NR algorithm if a NaN is found.
• Adding a Changelog.
• more sanitizing for the statistical tests =)

####0.4.0

• CoxFitter implements Cox Proportional Hazards model in lifelines.
• lifelines moves the wheels distributions.
• tests in the statistics module now prints the summary (and still return the regular values)
• new BaseFitter class is inherited from all fitters.