mle-hyperopt 0.0.10

Machine Learning Experiment Hyperparameter Optimization

Lightweight Hyperparameter Optimization 🚂

The mle-hyperopt package provides a simple and intuitive API for hyperparameter optimization of your Machine Learning Experiment (MLE) pipeline. It supports real, integer & categorical search variables and single- or multi-objective optimization.

Core features include the following:

• API Simplicity: strategy.ask(), strategy.tell() interface & space definition.
• Strategy Diversity: Grid, random, coordinate search, SMBO & wrapping FAIR's nevergrad, Successive Halving, Hyperband, Population-Based Training.
• Search Space Refinement based on the top performing configs via strategy.refine(top_k=10).
• Export of configurations to execute via e.g. python train.py --config_fname config.yaml.
• Storage & reload search logs via strategy.save(<log_fname>), strategy.load(<log_fname>).

For a quickstart check out the notebook blog 📖.

The API 🎮

from mle_hyperopt import RandomSearch

# Instantiate random search class
strategy = RandomSearch(real={"lrate": {"begin": 0.1,
                                        "end": 0.5,
                                        "prior": "log-uniform"}},
                        integer={"batch_size": {"begin": 32,
                                                "end": 128,
                                                "prior": "uniform"}},
                        categorical={"arch": ["mlp", "cnn"]})

# Simple ask - eval - tell API
configs = strategy.ask(5)
values = [train_network(**c) for c in configs]
strategy.tell(configs, values)

Implemented Search Types 🔭

	Search Type	Description	search_config
	GridSearch	Search over list of discrete values	-
	RandomSearch	Random search over variable ranges	refine_after, refine_top_k
	CoordinateSearch	Coordinate-wise optimization with fixed defaults	order, defaults
	SMBOSearch	Sequential model-based optimization (Hutter et al., 2011)	base_estimator, acq_function, n_initial_points
	NevergradSearch	Multi-objective nevergrad wrapper	optimizer, budget_size, num_workers
	HalvingSearch	Successive Halving (Karmin et al., 2013)	min_budget, num_arms, halving_coeff
	HyperbandSearch	Hyperband (Li et al., 2018)	max_resource, eta
	PBTSearch	Population-Based Training (Jaderberg et al., 2017)	explore, exploit

Variable Types & Hyperparameter Spaces 🌍

	Variable	Type	Space Specification
	real	Real-valued	Dict: begin, end, prior/bins (grid)
	integer	Integer-valued	Dict: begin, end, prior/bins (grid)
	categorical	Categorical	List: Values to search over

Installation ⏳

A PyPI installation is available via:

pip install mle-hyperopt

If you want to get the most recent commit, please install directly from the repository:

pip install git+https://github.com/mle-infrastructure/mle-hyperopt.git@main

Search Method Highlights 🔎

Grid Search 🟥

strategy = GridSearch(
    real={"lrate": {"begin": 0.1,
                    "end": 0.5,
                    "bins": 5}},
    integer={"batch_size": {"begin": 1,
                            "end": 5,
                            "bins": 1}},
    categorical={"arch": ["mlp", "cnn"]},
    fixed_params={"momentum": 0.9})  # Add fixed param setting to each config

configs = strategy.ask()

Hyperband 🎸

strategy = HyperbandSearch(
    real={"lrate": {"begin": 0.1,
                    "end": 0.5,
                    "prior": "uniform"}},
    integer={"batch_size": {"begin": 1,
                            "end": 5,
                            "prior": "log-uniform"}},
    categorical={"arch": ["mlp", "cnn"]},
    search_config={"max_resource": 81,
                   "eta": 3},
    seed_id=42,  # Fix randomness for reproducibility
    verbose=True)

configs = strategy.ask()

Population-Based Training 🦎

strategy = PBTSearch(
    real={"lrate": {"begin": 0.1,
                    "end": 0.5,
                    "prior": "uniform"}}
    search_config={
        "exploit": {"strategy": "truncation", "selection_percent": 0.2},
        "explore": {"strategy": "perturbation", "perturb_coeffs": [0.8, 1.2]},
        "steps_until_ready": 4,
        "num_workers": 10,
    },
    maximize_objective=True  # Max score instead of min
)

configs = strategy.ask()

Further Options 🚴

Saving & Reloading Logs 🏪

# Storing & reloading of results from .json/.yaml/.pkl
strategy.save("search_log.json")
strategy = RandomSearch(..., reload_path="search_log.json")

# Or manually add info after class instantiation
strategy = RandomSearch(...)
strategy.load("search_log.json")

Search Decorator 🧶

from mle_hyperopt import hyperopt

@hyperopt(strategy_type="Grid",
          num_search_iters=25,
          real={"x": {"begin": 0., "end": 0.5, "bins": 5},
                "y": {"begin": 0, "end": 0.5, "bins": 5}})
def circle(config):
    distance = abs((config["x"] ** 2 + config["y"] ** 2))
    return distance

strategy = circle()

Storing Configuration Files 📑

# Store 2 proposed configurations - eval_0.yaml, eval_1.yaml
strategy.ask(2, store=True)
# Store with explicit configuration filenames - conf_0.yaml, conf_1.yaml
strategy.ask(2, store=True, config_fnames=["conf_0.yaml", "conf_1.yaml"])

Storing Checkpoint Paths 🛥️

# Ask for 5 configurations to evaluate and get their scores
configs = strategy.ask(5)
values = ...
# Get list of checkpoint paths corresponding to config runs
ckpts = [f"ckpt_{i}.pt" for i in range(len(configs))]
# `tell` parameter configs, eval scores & ckpt paths
# Required for Halving, Hyperband and PBT
strategy.tell(configs, scores, ckpts)

Retrieving Top Performers & Visualizing Results 📉

# Get the top k best performing configurations
id, configs, values = strategy.get_best(top_k=4)

# Plot timeseries of best performing score over search iterations
strategy.plot_best()

# Print out ranking of best performers
strategy.print_ranking(top_k=3)

Refining the Search Space of Your Strategy 🪓

# Refine the search space after 5 & 10 iterations based on top 2 configurations
strategy = RandomSearch(real={"lrate": {"begin": 0.1,
                                        "end": 0.5,
                                        "prior": "log-uniform"}},
                        integer={"batch_size": {"begin": 1,
                                                "end": 5,
                                                "prior": "uniform"}},
                        categorical={"arch": ["mlp", "cnn"]},
                        search_config={"refine_after": [5, 10],
                                       "refine_top_k": 2})

# Or do so manually using `refine` method
strategy.tell(...)
strategy.refine(top_k=2)

Note that the search space refinement is only implemented for random, SMBO and nevergrad-based search strategies.

Simple Command Line interface ⌨️

You can also directly launch a search for your applications. This requires a couple of things: A python script <script>.py containing a function main(config), which runs your simulation for a given configuration dictionary. It should return a single scalar performance score, which will be logged.

def main(config):
    ...
    return score

Furthermore, you will need a search configuration <search>.yaml file and can add default fixed parameter settings in <base>.yaml.

mle-search <script>.py -base <base>.yaml -search <search>.yaml -iters <search_iters>

Have a look at the example, which can be executed via mle-search run_mle_search.py -search search.yaml -base base.yaml. You can reload a previous search log by adding the option -reload.

Citing the MLE-Infrastructure ✏️

If you use mle-hyperopt in your research, please cite it as follows:

@software{mle_infrastructure2021github,
  author = {Robert Tjarko Lange},
  title = {{MLE-Infrastructure}: A Set of Lightweight Tools for Distributed Machine Learning Experimentation},
  url = {http://github.com/mle-infrastructure},
  year = {2021},
}

Development 👷

You can run the test suite via python -m pytest -vv tests/. If you find a bug or are missing your favourite feature, feel free to create an issue and/or start contributing 🤗.