# scikit-survival 0.18.0 released

I’m pleased to announce the release of scikit-survival 0.18.0, which adds support for scikit-learn 1.1.

In addition, this release adds the `return_array`

argument to all models providing
`predict_survival_function`

and `predict_cumulative_hazard_function`

.
That means you can now choose, whether you want to have the survival
(cumulative hazard function) automatically evaluated at the unique event
times. This is particular useful for plotting. Previously, you would have
to evaluate each survival function before plotting:

```
estimator = CoxPHSurvivalAnalysis()
estimator.fit(X_train, y_train)
pred_surv = estimator.predict_survival_function(
X_test
)
times = pred_surv[0].x
for surv_func in pred_surv:
plt.step(times, surv_func(times), where="post")
```

Now, you can pass `return_array=True`

and directly get probabilities
of the survival function:

```
estimator = CoxPHSurvivalAnalysis()
estimator.fit(X_train, y_train)
pred_surv_probs = estimator.predict_survival_function(
X_test, return_array=True
)
times = estimator.event_times_
for probs in pred_surv_probs:
plt.step(times, probs, where="post")
```

Finally, support for Python 3.7 has been dropped and the minimal required version of the following dependencies are raised:

- numpy 1.17.3
- pandas 1.0.5
- scikit-learn 1.1.0
- scipy 1.3.2

For a full list of changes in scikit-survival 0.18.0, please see the release notes.

## Install

Pre-built conda packages are available for Linux, macOS (Intel), and Windows, either

via pip:

```
pip install scikit-survival
```

or via conda

```
conda install -c sebp scikit-survival
```