HyperParameter

A hyper-parameter library for researchers, data scientists and machine learning engineers.

• HyperParameter
• Quick Start
  • Object-Style API:
  • Scoped Parameter
  • Predefined Parameter
• Examples
  • parameter tunning for researchers
  • experiment tracing for data scientists
  • design-pattern for system 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.