lifelines | Survival analysis in Python | Machine Learning library
kandi X-RAY | lifelines Summary
kandi X-RAY | lifelines Summary
What is survival analysis and why should I learn it? Survival analysis was originally developed and applied heavily by the actuarial and medical community. Its purpose was to answer why do events occur now versus later under uncertainty (where events might refer to deaths, disease remission, etc.). This is great for researchers who are interested in measuring lifetimes: they can answer questions like what factors might influence deaths?.
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Top functions reviewed by kandi - BETA
- Fit the model
- Add intercept column
- Convert x to a list
- Transform a dataframe into a dataframe
- Fit a CoxPhFitter
- Compute central_stats
- Compute standard errors
- Returns a semi - parametric pmfitter
- Fit an interval censored model
- Print a summary of the data table
- Plot a qq plot
- Return a pandas Series containing the percentile of the model
- Print a summary of the model
- Compute the optimal gradient of the regression
- Fit left censored distribution
- Print a summary table
- Plot the model
- Predict the hazard of a hazard
- Fit the Normalization model
- Compute the survival difference between a fixed point and a fixed point
- Estimate the Bayesian Estimator estimator
- Fit the CoxPhFitter
- Add a covariance matrix to a dataframe
- Compute the pairwise log rank test
- Fit the regression model to a DataFrame
- Plot interval censored lifetimes
lifelines Key Features
lifelines Examples and Code Snippets
Lines of Code : 46
License : No License
Copyimport lifelines
from sklearn_lifelines.estimators_wrappers import CoxPHFitterModel
from sklearn.pipeline import make_pipeline
from sklearn.cross_validation import train_test_split
from patsylearn import PatsyTransformer
data = lifelines.datasets.lo {
"override": false,
"lifelines": [
{
"name": ".*Controller.*",
"annotation": ".*Controller",
"entryPoint": true
},
{
"name": ".*Service.*"
},
{
"name": ".*Repository.*",
"annotation": ".*(R Perl 5.005 or later
ActivePerl5 Build Number 520 or later has been reported to work
Date::Manip.pm to work with dates
Text::Soundex.pm to use soundex
Parse::RecDescent.pm to use lines2perl
Roman.pm to use the LifeLines function Community Discussions
Trending Discussions on lifelines
QUESTION
I am new to survival analysis and I have been reading many research paper where the authors report adjusted (age and gender) and unadjusted hazard ratios along with confidence intervals. I am currently using CoxPHFitter from lifelines python package but I am unable to extract hazard ratios. I have followed many links e.g. https://databricks.com/notebooks/survival_analysis/survival_analysis_03_modeling_hazards.html and https://towardsdatascience.com/survival-analysis-part-a-70213df21c2e but none of them give any details on how to extract hazard ratio along with confidence intervals for adjusted or unadjusted cox regression. Using the "baseline_hazard" does give hazard ratio for the intervals but no confidence interval (I am not sure whether this is the right variable to look at) The "confidence_intervals" provides the confidence intervals of the covariates but I am looking for hazard ratio of the fitted model. Can anyone please help me with this? I am new to this analysis
...ANSWER
Answered 2022-Mar-15 at 12:28The hazard ratios (labelled exp(coef)) and confidence intervals are available in the cph.summary, and in a prettier format with cph.print_summary().
QUESTION
In an UML communication diagram, the objects that interact with each other are visually connected with lines along which sequenced messages can circulate in both directions. Lines between objects usually represent links, i.e. instances of an association. But objects can exchange message even if they are not associated (e.g. if one object is passed as parameter or return to the other).
I wish to represent such a message exchange between objects that are not associated. But I would like to disambiguate and clarify that there is no direct association between the involved objects. Do the UML specs allow to express this without creating user-defined stereotypes?
Moreover do the current UML specifications define somewhere a term for the relation between objects in a communication diagram that interact ? And is it possible to further specify in the diagram how the communicating objects know about each other?
More research done before asking the questionI am currently re-reading "The UML User Guide, 2nd edition" by Grady Booch, James Rumbaugh and Ivar Jacobson, a great book that explains the UML specifications in reader-friendly plaintext. It's the updated UML 2 edition of the book and I could map back to the UML 2.5.1 specifications most of their claims.
In chapter 16 on interactions however, they explain that objects communicate along links:
A link specifies a path along which one object can dispatch a message to another (or the same) object. (...) If you need to be more precise about how that path exists, you can adorn the appropriate end of the link with one of the following constraints:
- association: (...) object is visible by association
- self: (...) object is visible because it is the dispatcher of the operation
- global: (...) object is visible because it is in an enclosing scope
- local: (...) object is visible because it is in a local scope
- parameter: (...) object is visible because it is a parameter
In chapter 19, they explain for communication diagrams that objects involved in the interaction are shown interlinked:
you render the links that connect these objects as the arcs of this graph. The links may have rolenames to identify them. Finally, you adorn these links with the messages that objects send and receive.
This seemed straightforward. So I looked for the corresponding UML 2.5.1 specifications:
- Links are solely defined as instances of associations.
- For communication diagrams, there is no mention at all in section 17.9 of links nor communication channels. Figure 17.26 shows interlinked objects (i.e. lifelines), but the lines linking the objects AND the arrows representing the messages along this line seem both to be graphically defined as "messages". This seems very ambiguous to me.
- Moreover I have not found any reference to constraints, keywords or pre-defined stereotypes that could describe how the objects know about each other in a particular diagram and that could justify a communication channel between them.
- I could find
«association»,«local»,«global»,«parameter»(with the same meaning as above) defined as stereotypes in the obsolete UML 1.4 specification, but no longer in the current specs.
Hence my question.
...ANSWER
Answered 2021-Nov-09 at 22:48The specification doesn't say this explicitely, but I think this is the definition of link used:
Two objects are said to be linked, when they can interact with each other.
There are several places in the specification that talk about links. The most general one I found in 11.2.3.3
Each link may be realized by something as simple as a pointer or by something as complex as a network connection, and may represent the possibility of instances being able to communicate because their identities are known by virtue of being passed in as parameters, held in variables or slots, or even because the communicating instances are the same instance.
From this I derived my simple definition.
So, I disagree, links are not "solely defined as instances of associations". This misunderstanding probably stems from the fact, that specifying links with InstanceSpecifications is only possible for Associations: Each InstanceSpecification that wants to have slots for the linked InstanceSpecifications must have an Association as Classifier. But - links can be there even if they are not specified. And they can be specified by other model elements.
These other link specifying model elements include connectors in composite structure diagrams or messages in interaction diagrams or attributes and parameters in class diagrams.
Communication diagrams are just a way to show an interaction. They have the same underlying metamodel instances as a sequence diagram. Therefore, even though there are lines in between the lifelines, there is no corresponding model element. They are just there to have a place for attaching the message symbols (see Table 17.4 Graphic Paths Included in Communications Diagrams).
It is possible for a message to reference a connector. So, you can view the line in the communication diagram as a visual representation of this connector. However, it is not necessary to model the connector explicitely. There are tools that will require a connector, but in the specification the connector is optional.
As you say, there are no stereotypes to describe how objects know each other. You would need to define your own. However, there are other possibilities: A Lifeline represents a connectable element, which can either be a parameter or an attribute (or a variable, but nobody uses them). By looking at the represented attribute, you can find out, whether it is a global or local attribute and whether it is an association end.
Having said that, there is a catch. Officially all represented elements must belong directly or indirectly to the context class of the interaction (same_classifier constraint in 17.12.17.5). In most cases I encounter, this context is just the system. This means, all attributes must be global. I had a long conversation with the author of this section, and found out, that the idea was, that an interaction should belong to a collaboration and the connection to the system is via CollaborationUses and roleBindings. This is the reason, why MagicDraw automatically creates a Collaboration for each Interaction.
This adds another level of indirection, which solves, as we know ;-) all problems in computer science. Fortunately, most tools don't enforce this rule, so we are free to let our lifelines represent attributes and parameters of real components of our systems.
QUESTION
I am making a 'Who Wants To Be A Millionaire' game. I am at the later stages, and I am trying to store the user names and scores, but when I call getUser() from another method it comes up as null, I printed the value of getUser() out to make sure it actually storing the values. Here is my code
...ANSWER
Answered 2021-Aug-22 at 15:36Where do you call storeUserHighscore method ?
Also, I believe the first if statement is un-necessary.
refactor to be like this.
QUESTION
I am trying to append two data frames.
The dataframes have duplicate columns which should be merged into one with new values added as extra rows.
...ANSWER
Answered 2021-Jul-29 at 10:18There is problem with duplicated columns names like here a, a columns, solution for deduplicated is use GroupBy.cumcount with DataFrame.stack for all DataFrames with duplicates:
QUESTION
I am building a multi-input, single-output CNN using Keras's functional API. There are SMILE data inputs which are 1D sequences and Proteins, which are also 1D sequences. I have the following data types and structures:
...ANSWER
Answered 2021-Jul-01 at 03:19problem is with the input dimensions, Conv1D expects input of shape 3D but in this case it 2D.
Conv1D needs to have a 3D shape data [batch_size, time_steps, feature_size]
Example,If we provide for each of the 50 batch samples, for each of the 2 time steps, a 100 dimensional vector: Input data shape should be something like,
QUESTION
I installed the python package lifelines on my terminal. The windows terminal is my terminal of choice, with a powershell and anaconda terminal that I often used.
I tried installing the package using the provided commands in the documentation:
pip install lifelines
and
conda install -c conda-forge lifelines
Both times the installation is marked as successfull. When I run Python within the terminal I can import the lifelines package without problem. However whem I import it on a jupyter notebook it yields a ModuleNotFoundError.
The base environment I use does not contain the lifelines package when I verify its contents using the Anaconda Navigator.
...ANSWER
Answered 2021-May-21 at 12:33The jupyter notebooks are run on Anaconda Powershell, and so are the environments and packages.
Installing on the Windows Powershell will never work. Running the conda install -c conda-forge lifelines in the Anaconda shell solved the issue.
So silly, yet so time consuming it is worth sharing.
QUESTION
I have a question concerning survival analysis. However, I have the following data (just an excerpt):
Now I am trying to do Survival Analysis with Python lifelines package. For example I want to find out if T-cells influence the Overall Survival (OS). But as far as I know, I need to categorizie the numer of T cells in different categories, like e.g. High T-Cell and Low T-Cell... Is that right? But how do I find out the best fitting Cut-Out? My plan is to show, that Tumor with High T-Cells have a better survival than low T-Cells. But how could I find the best cut-off-value to discriminate between High and Low T-Cell out of the data I have here.
Does anyone has an idea? A friend of mine said something about "ROC"-Analysis but I am really confused now... I would be glad about any help!
...ANSWER
Answered 2021-May-19 at 12:44The transformation of continuous variables into categorical variables is far from obvious. A first approach can be based on the existing literature, especially in medicine/biology. A review of the existing literature may be sufficient to create these classes. Another method can be based on the empirical distribution of the T-Cells variable, sometimes highlighting an "obvious" categorization. The use of an ROC curve can be a good idea but somehow I don't think it is necessary. Categorizing your variable in Kaplan-Meier type survival analyses is necessary, but if you use Cox models there is no need to categorize this variable. So I would advise you to turn to Cox regressions to conduct your survival analysis. A Cox regression would allow you to add several predictors in your modeling as well as interaction terms, which is more convenient.
QUESTION
I want to estimate cox models but when I try to run the code ,I have an error. it seems this problem about the coxphfitter().does any one here that solve this problem. I think the lifelines library can not compute coefficients with ML method .So here I copy errors and sample code .I should to say I write the code just for example and inputs not reall.
code
...ANSWER
Answered 2021-Feb-12 at 12:18The given clearly states the problem:
ConvergenceError: Convergence halted due to matrix inversion problems. Suspicion is high collinearity. Please see the following tips in the lifelines documentation: https://lifelines.readthedocs.io/en/latest/Examples.html#problems-with-convergence-in-the-cox-proportional-hazard-modelMatrix is singular.
Without the real data I can't give any further advice. But the lifelines documentation gives a lot of advice on this issue:
Convergence halted due to matrix inversion problems: This means that there is high collinearity in your dataset. That is, a column is equal to the linear combination of 1 or more other columns. A common cause of this error is dummying categorical variables but not dropping a column, or some hierarchical structure in your dataset. Try to find the relationship by: adding a penalizer to the model, ex: CoxPHFitter(penalizer=0.1).fit(…) until the model converges. In the print_summary(), the coefficients that have high collinearity will have large (absolute) magnitude in the coefs column. using the variance inflation factor (VIF) to find redundant variables. looking at the correlation matrix of your dataset, or
This is very likely not an error caused by lifelines instead it is your data or how you apply the model on your data.
QUESTION
I am writing some python code to do Kaplan-Meier (KM) curves using the KM Fitter and usually plot 4 curves in the same graph to compare different groups. The basic way to get a KM curve is:
from lifelines import KaplanMeierFitter
#Create the KMF object
KM_curve = KaplanMeierFitter()
#Give data to object. Status is 0 if alive, 1 if deceased (in my case)
KM_curve.fit (durations=My_Data["Time"], event_observed=My_Data["Status"])
#I do a figure in which I use this line 4 times (one per group)
KM_curve.plot(ci_show=False)
With those 4 lines of code and a pandas dataframe (here called My_Data) the KM Fitter automatically does all the calculations and plotting, but I was wondering if anyone knows how to stop the curve prematurely. I have done around 50 different graphs, they look nice and give me the info I need, but sometimes the last part of some curves dramatically drops to 0% (vertically) or very close to it. That is weird since none of my groups has 0 survivors at the end of my x-axis [See in this example, the red line https://i.stack.imgur.com/bn6Vy.png ]
I did read that the KM curves are good to see trends in the middle section, but the last part of the curves may be misleading and has to be examined carefully. That is especially true if there are not enough patients left in that group and thus, the %survival estimate drops dramatically. Someone who does bioinformatics told me she usually stops plotting the curve whenever 10% of patients are left, to prevent this issue. Is it possible to do that in python KMF?
...ANSWER
Answered 2020-Dec-07 at 18:36There are few ways to achieve this:
1.
QUESTION
I am using CoxPH implementation of lifelines package in python. Currently, results are in tabular view of coefficients and related stats and can be seen with print_summary(). Here is an example
...ANSWER
Answered 2020-Jun-25 at 13:20you can access the c-index with cph.concordance_index_ - and you could put this into a list or dataframe if you wish.
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Install lifelines
You can use lifelines like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
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