mle-hyperopt 0.0.4

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 around FAIR's nevergrad.
• 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	base_estimator, acq_function, n_initial_points
	NevergradSearch	Multi-objective nevergrad wrapper	optimizer, budget_size, num_workers

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

Alternatively, you can clone this repository and afterwards 'manually' install it:

git clone https://github.com/mle-infrastructure/mle-hyperopt.git
cd mle-hyperopt
pip install -e .

Further Options 🚴

Saving & Reloading Logs 🏪

# Storing & reloading of results from .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"])

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.

Development & Milestones for Next Release

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 contact me @RobertTLange or create an issue :hugs:.

• Robust type checking with isinstance(self.log[0]["objective"], (float, int, np.integer, np.float))
• Add improvement method indicating if score is better than best stored one
• Fix logging message when log is stored
• Add save option for best plot
• Make json serializer more robust for numpy data types
• Make sure search space refinement works for different batch sizes
• Add args, kwargs into decorator
• Check why SMBO can propose same config multiple times. Add Hutter reference.