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Automates machine learning and other computer experiments

Project description


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experitur automates machine learning and other computer experiments. Includes grid search and resuming aborted experiments. No lock-in, all your data is easily accessible in the text-based, machine-readable YAML format.


Experiment description

Every experiment is described in a regular python file. The @experiment decorator is used to mark experiment entry-points.

from experitur import experiment

        "parameter_1": [1,2,3],
        "parameter_2": ["a", "b", "c"],
def example(trial):
    """This is an example experiment."""

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.

Entry point

An experiment is a regular function that is decorated with @experiment (unless it is abstract or derived). Upon execution, the function gets called with the current trial. It may return a result dictionary.

Signature: (trial) -> dict

from experitur import experiment

        "parameter_1": [1,2,3],
        "parameter_2": ["a", "b", "c"],
def example(trial):
    """This is an example experiment."""
    print("parameters:", pformat(parameters))
    return {}

Now, you can run the experiment:

$ experitur run

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

Multiple experiments

The Python file can contain multiple experiments:

from experitur import experiment

def example1(trial):

def example2(trial):

Experiment inheritance

One experiment may inherit the settings of another, using the parent parameter.

from experitur import experiment

def example1(trial):

# Derived  with own entry point:
def example2(trial):

# Derived  with inherited entry point:
example3 = experiment("example3", parent=example2)

Parameter substitution

experitur includes 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.

        "a1": [1],
        "a2": [2],
        "b": [1, 2],
        "a": ["{a_{b}}"],
def example(trial):
$ experitur run parsub

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

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

  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.


This feature is especially useful if you want to run your experiments for different datasets but need slightly different settings for each dataset.

Let's assume we have two datasets, "bees" and "flowers".

            "dataset": ["bees", "flowers"],
            "dataset_fn": ["/data/{dataset}/index.csv"],
            "bees-crop": [10],
            "flowers-crop": [0],
            "crop": ["{{dataset}-crop}"]
def example(trial):

The experiment will be executed once for each dataset, with trial["crop"]==10 for the "bees" dataset and trial["crop"]==0 for the "flowers" dataset.

The trial object

Every experiment receives a trial object that allows access to the parameters and meta-data of the trial.

Parameters are accessed with the [] operator (e.g. trial["a"]), meta-data is accessed with the . operator (e.g. trial.wdir).

Access of parent data



When experitur executes a script, it creates the following file structure in the directory where the DOX file is located:

+- script/
|  +- experiment_id/
|  |  +- trial_id/
|  |  |  +- experitur.yaml
|  |  ...
|  ...

<script>/<experiment_id>/<trial_id>/experitur.yaml contains the parameters and the results from a trial, e.g.:

callable: example.experiment1
experiment: experiment1
id: experiment1/a-1_b-3
parameters: {a: 1, b: 3}
parent_experiment: null
result: null
success: true
time_end: 2019-06-07 14:22:41.697925
time_start: 2019-06-07 14:22:41.697837
wdir: examples/example/experiment1/a-1_b-3

Most items should be self-explanatory. parameters are the parameters passed to the entry point. id is derived from the parameters that are varied in the parameter grid. This way, you can easily interpret the file structure.

Collecting results

Use experitur collect to collect all the results (including parameters and metadata) of all trials of a lab book into a single CSV file located at script/results.csv.

Calling functions and default parameters

Your experiment function might call other functions that have default parameters. experitur gives you some utility functions that extract these default parameters adds them to the list of parameters.

For the following examples, let's assume trial["p_a"]=1 and trial["p_b"]=2.

  • trial.without_prefix(prefix: str, parameters: dict) -> dict: Extract all parameters that start with prefix.

    trial.without_prefix("p_") == {"a": 1, "b": 2}
  • trial.apply(prefix: str, callable_: callable, *args, **kwargs): Call callable_ with the parameters starting with prefix.

    trial.apply("p_", fun, 10, c=20)
    # is the same as
    fun(10, a=1, b=2, c=20)
  • trial.record_defaults(prefix, [callable_,] **defaults): Set default values for parameters that were not set previously. Values in defaults override default parameters of callable_.

    def foo(a=1, b=2, c=3):
    set_default_parameters("foo_", parameters, foo, c=4)
    # is the same as
    parameters.setdefault("foo_a", 1)
    parameters.setdefault("foo_b", 2)
    parameters.setdefault("foo_c", 4)

It is a good idea to make use of set_default_parameters and apply_parameters excessively. This way, your result files always contain the full set of parameters.

For a simple example, see examples/


experitur is packaged on PyPI.

pip install experitur

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


  • examples/ A very basic example showing the workings of set_default_parameters and apply_parameters.
  • examples/ Try different parameters of sklearn.svm.SVC to classify handwritten digits (the MNIST test set). Run the example, add more parameter values and see how experitur skips already existing configurations during the next run.


experitur is under active development, so any user feedback, bug reports, comments, suggestions, or pull requests are highly appreciated. Please use the bug tracker and fork the repository.


experitur is tested with Python 3.5, 3.6 and 3.7.

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