hyparam

Container for hyper parameter tuning in machine learning.

Example

Load programmatically

You can add parameters to the parameter search space by using the following methods.

python hp = HyperParameters() hp.add_linspace("learning_rate", 0.1, 0.3, 3) hp.add_switch("use_test_dataset") hp.add_range("epochs", 10, 30, 10) hp.add_list("myvar", [1.0, 12.0, 8.0])

Load from file

You can instead specify a parameter space in a YAML configuration file.

```yaml learning_rate: type: linspace setup: lower: 0.1 upper: 0.3 num: 3

usetestdataset: type: switch

epochs: type: range setup: start: 10 stop: 30 step: 10

myvar: type: list setup: values: [1.0, 12.0, 8.0] ```

This is loaded into Python as follows.

python hp = HyperParameters.from_file(file_name)

Iterating over the parameter space

In both the above examples, you can iterate over the parameter space using the choices method. See the examples in the example directory. You should expect the following output when you run these.

choice(learning_rate=0.1, use_test_dataset=True, epochs=10, myvar=1.0) choice(learning_rate=0.1, use_test_dataset=True, epochs=10, myvar=12.0) choice(learning_rate=0.1, use_test_dataset=True, epochs=10, myvar=8.0) choice(learning_rate=0.1, use_test_dataset=True, epochs=20, myvar=1.0) choice(learning_rate=0.1, use_test_dataset=True, epochs=20, myvar=12.0) choice(learning_rate=0.1, use_test_dataset=True, epochs=20, myvar=8.0) choice(learning_rate=0.1, use_test_dataset=False, epochs=10, myvar=1.0) choice(learning_rate=0.1, use_test_dataset=False, epochs=10, myvar=12.0) choice(learning_rate=0.1, use_test_dataset=False, epochs=10, myvar=8.0) choice(learning_rate=0.1, use_test_dataset=False, epochs=20, myvar=1.0) choice(learning_rate=0.1, use_test_dataset=False, epochs=20, myvar=12.0) choice(learning_rate=0.1, use_test_dataset=False, epochs=20, myvar=8.0) choice(learning_rate=0.2, use_test_dataset=True, epochs=10, myvar=1.0) choice(learning_rate=0.2, use_test_dataset=True, epochs=10, myvar=12.0) choice(learning_rate=0.2, use_test_dataset=True, epochs=10, myvar=8.0) choice(learning_rate=0.2, use_test_dataset=True, epochs=20, myvar=1.0) choice(learning_rate=0.2, use_test_dataset=True, epochs=20, myvar=12.0) choice(learning_rate=0.2, use_test_dataset=True, epochs=20, myvar=8.0) choice(learning_rate=0.2, use_test_dataset=False, epochs=10, myvar=1.0) choice(learning_rate=0.2, use_test_dataset=False, epochs=10, myvar=12.0) choice(learning_rate=0.2, use_test_dataset=False, epochs=10, myvar=8.0) choice(learning_rate=0.2, use_test_dataset=False, epochs=20, myvar=1.0) choice(learning_rate=0.2, use_test_dataset=False, epochs=20, myvar=12.0) choice(learning_rate=0.2, use_test_dataset=False, epochs=20, myvar=8.0) choice(learning_rate=0.3, use_test_dataset=True, epochs=10, myvar=1.0) choice(learning_rate=0.3, use_test_dataset=True, epochs=10, myvar=12.0) choice(learning_rate=0.3, use_test_dataset=True, epochs=10, myvar=8.0) choice(learning_rate=0.3, use_test_dataset=True, epochs=20, myvar=1.0) choice(learning_rate=0.3, use_test_dataset=True, epochs=20, myvar=12.0) choice(learning_rate=0.3, use_test_dataset=True, epochs=20, myvar=8.0) choice(learning_rate=0.3, use_test_dataset=False, epochs=10, myvar=1.0) choice(learning_rate=0.3, use_test_dataset=False, epochs=10, myvar=12.0) choice(learning_rate=0.3, use_test_dataset=False, epochs=10, myvar=8.0) choice(learning_rate=0.3, use_test_dataset=False, epochs=20, myvar=1.0) choice(learning_rate=0.3, use_test_dataset=False, epochs=20, myvar=12.0) choice(learning_rate=0.3, use_test_dataset=False, epochs=20, myvar=8.0)

Install

From source

In a new terminal: 1. Clone repository: - (ssh) $ git clone git@github.com:cmower/hyparam.git, or - (https) $ git clone https://github.com/cmower/hyparam.git 2. Change directory: $ cd hyparam 3. Ensure pip is up-to-date: $ python -m pip install --upgrade pip 3. Install: $ pip install .