Short for python experiment utilities. This is a collection of scripts and machine learning experiment management tools that I use whenever I have to use python.

For a more complete discussion on my organization patterns for research codebases, look in the docs.

Maintaining a rigorous experiment structure can be labor intensive. As such, I've automated out many of the common pieces that I use in my research.

Experiments are encoded within JSON files. The JSON files should contain all of the information necessary to reproduce an experiment, including all parameters swept. Each of the parameter sweep specifications leads to a set of parameter permutations. Imagine the case where you are sweeping over 2 meta-parameters:

{
    "metaParameters": {
        "alpha": [0.01, 0.02, 0.04],
        "epsilon": [0.1, 0.2, 0.3]
    }
}

Here there are 9 total possible permutations: {alpha: 0.01, epsilon: 0.1}, {alpha: 0.01, epsilon: 0.2}, ...

These are indexed by a single numeric value. To run each permutation once, simply execute indices i \in [0..8]. To run each permutation twice, multiply by 2: i \in [0..17]. In general for n runs and p permutations: i \in [0..(n*p - 1)].

A collection of JSON serialization classes with associated utility methods.

Config:

Experiment utility configuration file. Specifies global configuration settings:

• save_path: directory format where experimental results will be stored
• log_path: directory where log files will be saved (e.g. stacktraces during experiments)
• experiment_directory: root directory where all of the experiment description files are located

The config file should be at the root level of the repository and should be named config.json.

.git
.gitignore
tests/
scripts/
src/
config.json

An example configuration file:

{
    "save_path": "results/{name}/{environment}/{agent}/{params}",
    "log_path": "~/scratch/.logs",
    "experiment_directory": "experiments"
}

getConfig:

Memoized global configuration loader. Will read config.json (only once) and return a Config object.

config = getConfig()
print(config.save_path) # -> 'results'

ExperimentDescription:

Main workhorse class of the library. Takes a dictionary desribing all configurable options of an experiment and serializes that dictionary. Provides a set of utility methods to run parameter sweeps in parallel and for storing data during experiments.

exp_dict = {
    'algorithm': 'SARSA',
    'environment': 'MountainCar',
    'metaParameters': {
        'alpha': [1.0, 0.5, 0.25, 0.125],
        'lambda': [1.0, 0.99, 0.98, 0.96]
    }
}
exp = ExperimentDescription(d)

permutable:

Gives a list of parameters that can be swept over. Using above example dictionary:

params = exp.permutable()
print(params) # -> { 'alpha': [1.0, 0.5, 0.25, 0.125], 'lambda': [1.0, 0.99, 0.98, 0.96] }

getPermutation:

Gives the i'th permutation of sweepable parameters. Handles wrapping indices, so can perform multiple runs of the same parameter setting by setting i large. In the above dictionary, there are 16 total parameter permutations.

params = exp.getPermutation(0)
print(params) # -> { 'alpha': 1.0, 'lambda': 1.0 }
params = exp.getPermutation(1)
print(params) # -> { 'alpha': 1.0, 'lambda': 0.99 }
params = exp.getPermutation(15)
print(params) # -> { 'alpha': 0.125, 'lambda': 0.96 }
params = exp.getPermutation(16)
print(params) # -> { 'alpha': 1.0, 'lambda': 1.0 }

numPermutations:

Gives the total number of parameter permutations.

num_params = exp.numPermutations()
print(num_params) # -> 16

getRun:

Get the run number based on wrapping the index. This is a count of how many times we've wrapped back around to the same parameter setting.

num = exp.getRun(0)
print(num) # -> 0
num = exp.getRun(12)
print(num) # -> 0
num = exp.getRun(16)
print(num) # -> 1
num = exp.getRun(32)
print(num) # -> 2

getExperimentName:

Returns the name of the experiment if stated in the dictionary: { 'name': 'MountainCar-v0', ... }. If not stated, will try to determine the name of the experiment based on the path to the JSON it is stored in (assuming experiments are stored in JSON files).

path = 'experiments/MountainCar-v0/sarsa.json'
with open(path, 'r') as f:
    d = json.load(path)
exp = ExperimentDescription(d, path)
name = exp.getExperimentName()
print(name) # -> d['name'] if available, or 'MountainCar-v0' if not.

interpolateSavePath:

Takes a parameter index and generates a path for saving results. The path depends on the configuration settings of the library (i.e. config.json). Note this uses an opinionated formatting for save paths and parameter string representations. The configuration file can specify ordering and high-level control over paths, but for more fine-tuned control over how these are saved, inherit from this class and overload this method. config.json:

{
    "save_path": "results/{name}/{environment}/{agent}/{params}"
}

path = exp.interpolateSavePath(0)
print(path) # -> 'results/MountainCar-v0/SARSA/alpha-1.0_lambda-1.0'

buildSaveContext:

Builds a FileSystemContext utility object that contains the save path for experimental results.

file_context = exp.buildSaveContext(0)
# make sure folder structure is built
file_context.ensureExists()
# get the path where results should be saved
path = file_context.resolve('returns.npy')
print(path) # -> '/results/MountainCar-v0/SARSA/alpha-1.0_lambda-1.0/returns.npy'
# save results
np.save(path, returns)

loadExperiment:

Loads an ExperimentDescription from a JSON file (preferred way to make ExperimentDescriptions).

exp = loadExperiment('experiments/MountainCar-v0/sarsa.json')

Collector:

A frame-based data collection utility. The collector stores some context---which index is currently being run, what is the current timestep, etc.--- and associates collected data with this context.

Example usage:

collector = Collector(
  config={
    # a dictionary mapping keys -> data preprocessors
    # for instance performing fixed-window averaging
    'return': Window(100),
    # or subsampling 1 of every 100 values
    'reward': Subsample(100),
    # or moving averages
    'error': MovingAverage(0.99),
    # or ignored entirely
    'special': Ignore(),
  },
  # by default, if a key is not mentioned above it is stored as-is
  # however this can be changed by passing a default preprocessor
  default=Identity()
)

# tell the collector what idx of the experiment we are currently processing
collector.setIdx(0)

for step in range(exp.max_steps):
  # tell the collector to increment the frame
  collector.next_frame()

  # these values will be associated with the current idx and frame
  collector.collect('reward', r)
  collector.collect('error', delta)

  # not all values need to be stored at each frame
  if step % 100 == 0:
    collector.collect('special', 'test value')

hours:

Takes an integer number of hours and returns a well-formatted time string.

time = hours(3)
print(time) # -> '2:59:59

gb:

Takes an integer number of gigabytes and returns a well-formatted memory string.

memory = gb(4)
print(memory) # -> '4G'

listIndices:

Returns an iterator over indices for each parameter permutation. Can specify a number of runs and will cycle over the permutations runs number of times.

for i in listIndices(exp, runs=2):
    print(i, exp.getRun(i)) # -> "0 0", "1 0", "2 0", ... "0 1", "1 1", ...