This notebook introduces the algorithms within Dask-GLM for Generalized Linear Models.

Starting the Dask Client is optional. It will provide a dashboard which is useful to gain insight on the computation.

The link to the dashboard will become visible when you create the client below. We recommend having it open on one side of your screen while using your notebook on the other side. This can take some effort to arrange your windows, but seeing them both at the same is very useful when learning.

In [ ]:

```
from dask.distributed import Client, progress
client = Client(processes=False, threads_per_worker=4,
n_workers=1, memory_limit='2GB')
client
```

In [ ]:

```
from dask_glm.datasets import make_regression
X, y = make_regression(n_samples=200000, n_features=100, n_informative=5, chunksize=10000)
X
```

In [ ]:

```
import dask
X, y = dask.persist(X, y)
```

*We also recommend looking at the "Graph" dashboard during execution if available*

In [ ]:

```
import dask_glm.algorithms
b = dask_glm.algorithms.admm(X, y, max_iter=5)
```

In [ ]:

```
b = dask_glm.algorithms.proximal_grad(X, y, max_iter=5)
```

The Dask-GLM project is nicely modular, allowing for different GLM families and regularizers, including a relatively straightforward interface for implementing custom ones.

In [ ]:

```
import dask_glm.families
import dask_glm.regularizers
family = dask_glm.families.Poisson()
regularizer = dask_glm.regularizers.ElasticNet()
b = dask_glm.algorithms.proximal_grad(
X, y,
max_iter=5,
family=family,
regularizer=regularizer,
)
```

In [ ]:

```
dask_glm.families.Poisson??
```

In [ ]:

```
dask_glm.regularizers.ElasticNet??
```

In [ ]:

```
```