Skip to main content

Automates machine learning and other computer experiments

Project description

experitur

Build Status codecov

Automates machine learning and other computer experiments. Includes grid search and resuming aborted experiments.

Lab notebook

Every experiment is described in a lab notebook. This is a text file with a YAML header, e.g. a Markdown file or a YAML file without further content:

---
# In this part of the document called the "experiment section", enclosed by "---", you describe the experiment(s).
id: example
parameter_grid:
    parameter_1: [1,2,3]
    parameter_2: [a,b,c]
---
# An example experiment
In this part of the document, you can write down any content you like. Markdown files are allowed to contain a YAML header, so this could be Markdown.

Parameter grid

The core of an experiment is its parameter grid. It works like sklearn.model_selection.ParameterGrid. Each parameter has a list of values that it can take. A number of trials is generated from the cross product of the values of each parameter.

Run function

Each experiment has a run setting. It points to a python function that receives a working directory and the parameters.

---
# examle.md
id: example
run: "experitur.examples.echo:run"
parameter_grid:
    a: [1,2]
    b: [a,b]
---
# echo.py
from pprint import pprint

def run(working_directory, parameters):
    print(working_directory)
    pprint(parameters)

Now, you can run the experiment:

$ experitur run example.md
Running example.md...
Independent parameters: ['a', 'b']
Trial 0: a-1_b-a
  0% (0/4) [               ] eta --:-- /
    a: 1
    b: a
example/example/a-1_b-a
{'a': 1, 'b': 'a'}
Trial 1: a-1_b-b
 25% (1/4) [###            ] eta --:-- -
    a: 1
    b: b
example/example/a-1_b-b
{'a': 1, 'b': 'b'}
Trial 2: a-2_b-a
 50% (2/4) [#######        ] eta 00:01 \
    a: 2
    b: a
example/example/a-2_b-a
{'a': 2, 'b': 'a'}
Trial 3: a-2_b-b
 75% (3/4) [###########    ] eta 00:01 |
    a: 2
    b: b
example/example/a-2_b-b
{'a': 2, 'b': 'b'}
Overall: 0.003s
  a-1_b-a: 0.000s (13%)
  a-2_b-a: 0.000s (8%)
  a-2_b-b: 0.000s (8%)
  a-1_b-b: 0.000s (8%)

As you can see, run was called four times with every combination of [1,2] x [a,b].

Multiple experiments

The experiment section can hold multiple experiments in a list:

---
- id: experiment_1
    parameter_grid:
        ...
- id: experiment_2
    parameter_grid:
        ...
---

Experiment inheritance

One experiment may inherit the settings of another, using the base property:

---
- id: experiment_1
    parameter_grid:
        a: [1, 2, 3]
- id: experiment_2
    base: experiment_1
    parameter_grid:
        b: [x, y, z]
        # In effect, experiment_2 also a parameter 'a' that takes the values 1,2,3.
---

Parameter substitution

experitur has a recursive parameter substitution engine. Each value string is treated as a recursive format string and is resolved using the whole parameter set of a trial.

---
id: parsub
run: "experitur.examples.echo:run"
parameter_grid:
    a_1: [foo]
    a_2: [bar]
    a: ["{a_{b}}"]
    b: [1,2]
---
$ experitur run parsub.md
Running parsub.md...
Independent parameters: ['b']
Trial 0: b-1
  0% (0/2) [               ] eta --:-- /
    a: foo
    a_1: foo
    a_2: bar
    b: 1
parsub/parsub/b-1
{'a': 'foo', 'a_1': 'foo', 'a_2': 'bar', 'b': 1}
Trial 1: b-2
 50% (1/2) [#######        ] eta --:-- -
    a: bar
    a_1: foo
    a_2: bar
    b: 2
parsub/parsub/b-2
{'a': 'bar', 'a_1': 'foo', 'a_2': 'bar', 'b': 2}
Overall: 0.002s
  b-1: 0.000s (18%)
  b-2: 0.000s (14%)

This way, you can easily run complicated setups with settings that depend on other settings.

Recursive format strings work like string.Formatter with two excpetions:

  1. Recursive field names: The field name itself may be a format string:

    format("{foo_{bar}}", bar="baz", foo_baz="foo") -> "foo"
    
  2. Literal output: If the format string consist solely of a replacement field and does not contain a format specification, no to-string conversion is performed:

    format("{}", 1) -> 1
    

    This allows the use of format strings for non-string values.

Installation

experitur is packaged on PyPI.

pip install experitur

Be warned that this package is currently under heavy development and anything might change any time!

Compatibility

experitur is tested with Python 3.5, 3.6 and 3.7.

Similar software

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for experitur, version 0.1.2
Filename, size File type Python version Upload date Hashes
Filename, size experitur-0.1.2-py3-none-any.whl (17.4 kB) File type Wheel Python version py3 Upload date Hashes View
Filename, size experitur-0.1.2.tar.gz (13.9 kB) File type Source Python version None Upload date Hashes View

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring DigiCert DigiCert EV certificate Facebook / Instagram Facebook / Instagram PSF Sponsor Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Salesforce Salesforce PSF Sponsor Sentry Sentry Error logging StatusPage StatusPage Status page