Generalized Linear Models
Contents
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Generalized Linear Models¶
This notebook introduces the algorithms within Dask-GLM for Generalized Linear Models.
Start Dask Client for Dashboard¶
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.
[1]:
from dask.distributed import Client, progress
client = Client(processes=False, threads_per_worker=4,
n_workers=1, memory_limit='2GB')
client
[1]:
Client
Client-5b571f45-0de1-11ed-a361-000d3a8f7959
| Connection method: Cluster object | Cluster type: distributed.LocalCluster |
| Dashboard: http://10.1.1.64:8787/status |
Cluster Info
LocalCluster
94f79b63
| Dashboard: http://10.1.1.64:8787/status | Workers: 1 |
| Total threads: 4 | Total memory: 1.86 GiB |
| Status: running | Using processes: False |
Scheduler Info
Scheduler
Scheduler-079365e3-ef5b-4539-84b1-973599540812
| Comm: inproc://10.1.1.64/9057/1 | Workers: 1 |
| Dashboard: http://10.1.1.64:8787/status | Total threads: 4 |
| Started: Just now | Total memory: 1.86 GiB |
Workers
Worker: 0
| Comm: inproc://10.1.1.64/9057/4 | Total threads: 4 |
| Dashboard: http://10.1.1.64:39345/status | Memory: 1.86 GiB |
| Nanny: None | |
| Local directory: /home/runner/work/dask-examples/dask-examples/machine-learning/dask-worker-space/worker-ptl8ho2_ | |
Make a random dataset¶
[2]:
from dask_glm.datasets import make_regression
X, y = make_regression(n_samples=200000, n_features=100, n_informative=5, chunksize=10000)
X
[2]:
|
[3]:
import dask
X, y = dask.persist(X, y)
Solve with a GLM algorithm¶
We also recommend looking at the “Graph” dashboard during execution if available
[4]:
import dask_glm.algorithms
b = dask_glm.algorithms.admm(X, y, max_iter=5)
Solve with a difference GLM algorithm¶
[5]:
b = dask_glm.algorithms.proximal_grad(X, y, max_iter=5)
/usr/share/miniconda3/envs/dask-examples/lib/python3.9/site-packages/dask/core.py:119: RuntimeWarning: overflow encountered in exp
return func(*(_execute_task(a, cache) for a in args))
Customizable with different families and regularizers¶
The Dask-GLM project is nicely modular, allowing for different GLM families and regularizers, including a relatively straightforward interface for implementing custom ones.
[6]:
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,
)
/usr/share/miniconda3/envs/dask-examples/lib/python3.9/site-packages/dask/core.py:119: RuntimeWarning: overflow encountered in exp
return func(*(_execute_task(a, cache) for a in args))
/usr/share/miniconda3/envs/dask-examples/lib/python3.9/site-packages/dask/core.py:119: RuntimeWarning: overflow encountered in exp
return func(*(_execute_task(a, cache) for a in args))
[7]:
dask_glm.families.Poisson??
[8]:
dask_glm.regularizers.ElasticNet??