core.components.benchmark.nvemd_reducer

core.components.benchmark.nvemd_reducer#

Copyright (c) Meta Platforms, Inc. and affiliates.

This source code is licensed under the MIT license found in the LICENSE file in the root directory of this source tree.

Classes#

NVEMDReducer

Benchmark reducer interface class.

Functions#

moving_avg(x[, window])

get_te_drift(filename)

Module Contents#

core.components.benchmark.nvemd_reducer.moving_avg(x, window=20)#
core.components.benchmark.nvemd_reducer.get_te_drift(filename)#
class core.components.benchmark.nvemd_reducer.NVEMDReducer(benchmark_name: str)#

Bases: fairchem.core.components.benchmark.benchmark_reducer.BenchmarkReducer

Benchmark reducer interface class.

Note

When running with the fairchemv2 cli, the job_config and runner_config attributes are set at runtime to those given in the config file.

See the Reducer interface class for implementation details.

job_config#

a managed attribute that gives access to the job config

Type:

DictConfig

runner_config#

a managed attributed that gives access to the calling runner config

Type:

DictConfig

benchmark_name#
property runner_type: type[fairchem.core.components.calculate.nve_md_runner.NVEMDRunner]#

The runner type this reducer is associated with.

join_results(results_dir: str, glob_pattern: str) → pandas.DataFrame#

Join results from multiple JSON files into a single DataFrame.

Parameters:

  • results_dir – Directory containing result files

  • glob_pattern – Pattern to match result files

Returns:

Combined DataFrame containing all results

save_results(results: list, results_dir: str) → None#

Save joined results to a compressed json file

Parameters:

  • results – results: Combined results from join_results

  • results_dir – Directory containing result files

compute_metrics(results: list, run_name: str) → pandas.DataFrame#

Compute Matbench discovery metrics for relaxed energy and structure predictions.

Parameters:

  • results – DataFrame containing prediction results with energy values

  • run_name – Identifier for the current evaluation run

Returns:

DataFrame containing computed metrics for different material subsets

save_metrics(metrics: pandas.DataFrame, results_dir: str) → None#

Save computed metrics to a compressed JSON file.

Parameters:

  • metrics – DataFrame containing the computed metrics

  • results_dir – Directory where metrics will be saved

log_metrics(metrics: pandas.DataFrame, run_name: str) → None#

Log metrics to the configured logger if available.

Parameters:

  • metrics – DataFrame containing the computed metrics

  • run_name – Name of the current run

save_state(checkpoint_location: str, is_preemption: bool = False) → bool#

Save the current state of the reducer to a checkpoint.

Parameters:

  • checkpoint_location – Location to save the checkpoint

  • is_preemption – Whether the save is due to preemption

Returns:

Success status of the save operation

Return type:

bool

load_state(checkpoint_location: str | None) → None#

Load reducer state from a checkpoint.

Parameters:

checkpoint_location – Location to load the checkpoint from, or None

Contents