A hyper-parameter library for researchers, data scientists and machine learning engineers.
Project description
HyperParameter
A hyper-parameter library for researchers, data scientists and machine learning engineers.
Quick Start
Object-Style API:
from hyperparameter import HyperParameter
params = HyperParameter(a=1, b={'c': 2})
params.a == 1 # True
params.b.c == 2 # True (nested parameter)
or becomes powerful with params()
:
params().a.b.c.getOrElse(3) # 3 (default value)
params().a.b.c(3) # 3 (shortcut for default value)
params().a.b.c = 4 # set value to param `a.b.c`
params().a.b.c(3) # 4 (default value is ignored)
Scoped Parameter
from hyperparameter import param_scope
# scoped parameter
with param_scope(a=1) as hp:
hp.a == 1 # True
or becomes powerful with nested scope
:
with param_scope(a=1) as hp:
with param_scope(a=2) as hp:
hp.a == 2 # True, a=2 for inner scope
hp.a == 1 # True, a=1 for outer scope
even more powerful when using param_scope
in function:
#change function behavior with scoped parameter:
def foo(arg):
# receive parameter using param_scope
with param_scope() as hp:
if (hp().param1.getOrElse(1) == 1):
return 1
else:
return 2
...
# call function with default parameter
foo() # 1
# passing parameter using param_scope
with param_scope(param1=2):
foo() # 2
Predefined Parameter
@auto_param #convert keyword arguments into hyper parameters
def model_train(X, y, learning_rate = 1.0, penalty = 'l1'):
LR = LogisticRegression(C=1.0,
lr=local_param('learning_rate'),
penalty=local_param('penalty'))
LR.fit(X, y)
# specify predefined parameter using `param_scope`
with param_scope('model_train.learning_rate=0.01'):
model_train(X, y)
Examples
parameter tunning for researchers
This example shows how to use hyperparameter in your research projects, and make your experiments reproducible.
experiment tracing for data scientists
This example shows experiment management with hyperparameter, and tracing the results with mlflow.tracing.
Todo.
design-pattern for system engineers
Todo.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
hyperparameter-0.2.0.tar.gz
(6.2 kB
view hashes)
Built Distribution
Close
Hashes for hyperparameter-0.2.0-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 46b8976437a2ad3226f88ade958903774ec219e8eaa8a93be8d34d704fc7d956 |
|
MD5 | 0efe2e139f6580ca6bdcc3e41b797389 |
|
BLAKE2b-256 | 0cd707f66cf7023d0981ab951a7334efa429272c73887779ab8c759cbe0307a2 |