def test_loaded_DenseModel_predicts_with_clipped_inputs(tmpdir):
hyperparameters = DenseHyperparameters(
["a", "b"],
["c"],
clip_config=PackerConfig({"a": {
"z": SliceConfig(None, 3)
}}),
)
model = DenseModel(["a", "b"], ["c"], hyperparameters)
nz = 5
dims = ["x", "y", "z"]
shape = (2, 2, nz)
arr = np.arange(np.prod(shape)).reshape(shape).astype(float)
input_data = xr.Dataset({
"a": (dims, arr),
"b": (dims, arr),
"c": (dims, arr + 1)
})
model.fit([input_data])
prediction = model.predict(input_data)
output_path = str(tmpdir.join("trained_model"))
fv3fit.dump(model, output_path)
model_loaded = fv3fit.load(output_path)
loaded_prediction = model_loaded.predict(input_data)
xr.testing.assert_allclose(prediction, loaded_prediction)
def open_model(config: MachineLearningConfig) -> MultiModelAdapter:
model_paths = config.model
models = []
for path in model_paths:
model = fv3fit.load(path)
rename_in = config.input_standard_names
rename_out = config.output_standard_names
models.append(RenamingAdapter(model, rename_in, rename_out))
return MultiModelAdapter(models)
def test_cli(
tmpdir,
use_local_download_path: bool,
use_validation_data: bool,
model_type: str,
hyperparameter_dict,
):
"""
Test of fv3fit.train command-line interface.
"""
config = get_config(
tmpdir, [], model_type, hyperparameter_dict, use_validation_data,
)
mock_load_batches.return_value = [config.mock_dataset for _ in range(6)]
if use_local_download_path:
config.args.local_download_path = os.path.join(str(tmpdir), "local_download")
cli_main(config.args)
fv3fit.load(config.args.output_path)
if use_local_download_path:
assert len(os.listdir(config.args.local_download_path)) > 0
def test_dump_and_load_default_maintains_prediction(model_type):
n_sample, n_tile, nx, ny, n_feature = 1, 6, 12, 12, 2
sample_func = get_uniform_sample_func(size=(n_sample, n_tile, nx, ny,
n_feature))
result = train_identity_model(model_type, sample_func=sample_func)
original_result = result.model.predict(result.test_dataset)
with tempfile.TemporaryDirectory() as tmpdir:
fv3fit.dump(result.model, tmpdir)
loaded_model = fv3fit.load(tmpdir)
loaded_result = loaded_model.predict(result.test_dataset)
xr.testing.assert_equal(loaded_result, original_result)
def test_fit_bptt_command_line(regtest, sample_dim_name, dt):
config_text = """
regularizer:
name: l2
kwargs:
l: 0.0001
optimizer:
name: Adam
kwargs:
learning_rate: 0.001
amsgrad: True
clipnorm: 1.0
hyperparameters:
n_hidden_layers: 3
n_units: 256
train_batch_size: 48
use_moisture_limiter: true
state_noise: 0.25
input_variables:
- a
- b
decrease_learning_rate_epoch: 1
decreased_learning_rate: 0.0001
total_epochs: 2
random_seed: 0
"""
np.random.seed(0)
training_dataset = get_train_dataset(sample_dim_name, dt)
with tempfile.TemporaryDirectory() as tmpdir:
arrays_dir = os.path.join(tmpdir, "arrays")
os.mkdir(arrays_dir)
for i in range(5):
filename = os.path.join(arrays_dir, f"arrays_{i}.nc")
training_dataset.to_netcdf(filename)
config_filename = os.path.join(tmpdir, "config.yaml")
with open(config_filename, "w") as f:
f.write(config_text)
outdir = os.path.join(tmpdir, "output")
subprocess.check_call(
["python", "-m", "fv3fit.train_bptt", arrays_dir, config_filename, outdir]
)
# as a minimum, test that output exists
assert os.path.isdir(outdir)
assert len(os.listdir(outdir)) > 0
# one model per epoch is saved
for sample_model_dir in sorted(os.listdir(outdir)):
loaded = fv3fit.load(os.path.join(outdir, sample_model_dir))
first_timestep = training_dataset.isel(time=0)
loaded_output = loaded.predict(first_timestep)
assert isinstance(loaded_output, xr.Dataset)
def test_constant_model_predict_after_dump_and_load(input_variables,
output_variables, nz):
gridded_dataset = get_gridded_dataset(nz)
outputs = get_first_columns(gridded_dataset, output_variables)
predictor = get_predictor(input_variables, output_variables, outputs)
with tempfile.TemporaryDirectory() as tempdir:
fv3fit.dump(predictor, tempdir)
predictor = fv3fit.load(tempdir)
ds_pred = predictor.predict(gridded_dataset)
assert sorted(list(ds_pred.data_vars.keys())) == sorted(output_variables)
for name in output_variables:
assert np.all(
stack_non_vertical(ds_pred[name]).values == outputs[name][None, :])
def test_dump_and_load(tmpdir):
derived_model = DerivedModel(
base_model, derived_output_variables=["net_shortwave_sfc_flux_derived"],
)
ds_in = xr.Dataset(
data_vars={
"input": xr.DataArray(np.zeros([3, 3, 5]), dims=["x", "y", "z"],),
"surface_diffused_shortwave_albedo": xr.DataArray(
np.zeros([3, 3]), dims=["x", "y"],
),
}
)
prediction = derived_model.predict(ds_in)
fv3fit.dump(derived_model, str(tmpdir))
loaded_model = fv3fit.load(str(tmpdir))
prediction_after_load = loaded_model.predict(ds_in)
assert prediction_after_load.identical(prediction)
def test_reloaded_model_gives_same_outputs(sample_dim_name, dt):
train_dataset = get_train_dataset(sample_dim_name, dt)
model = _BPTTTrainer(
sample_dim_name,
["a", "b"],
n_units=32,
n_hidden_layers=4,
kernel_regularizer=None,
train_batch_size=48,
optimizer="adam",
)
model.fit_statistics(train_dataset)
model.fit(train_dataset)
with tempfile.TemporaryDirectory() as tmpdir:
fv3fit.dump(model.predictor_model, tmpdir)
loaded = fv3fit.load(tmpdir)
first_timestep = train_dataset.isel(time=0)
reference_output = model.predictor_model.predict(first_timestep)
# test that loaded model gives the same predictions
loaded_output = loaded.predict(first_timestep)
xr.testing.assert_equal(reference_output, loaded_output)
def main(args):
logger.info("Starting diagnostics routine.")
with fsspec.open(args.data_yaml, "r") as f:
as_dict = yaml.safe_load(f)
config = loaders.BatchesLoader.from_dict(as_dict)
logger.info("Reading grid...")
if not args.grid:
# By default, read the appropriate resolution grid from vcm.catalog
grid = load_grid_info(args.grid_resolution)
else:
with fsspec.open(args.grid, "rb") as f:
grid = xr.open_dataset(f, engine="h5netcdf").load()
logger.info("Opening ML model")
model = fv3fit.load(args.model_path)
# add Q2 and total water path for PW-Q2 scatterplots and net precip domain averages
if any(["Q2" in v for v in model.output_variables]):
model = fv3fit.DerivedModel(model, derived_output_variables=["Q2"])
model_variables = _variables_to_load(model) + ["water_vapor_path"]
else:
model_variables = _variables_to_load(model)
output_data_yaml = os.path.join(args.output_path, "data_config.yaml")
with fsspec.open(args.data_yaml,
"r") as f_in, fsspec.open(output_data_yaml, "w") as f_out:
f_out.write(f_in.read())
batches = config.load_batches(model_variables)
predict_function = _get_predict_function(model, model_variables, grid)
batches = loaders.Map(predict_function, batches)
# compute diags
ds_diagnostics, ds_scalar_metrics = _compute_diagnostics(
batches,
grid,
predicted_vars=model.output_variables,
n_jobs=args.n_jobs)
ds_diagnostics = ds_diagnostics.update(grid)
# save model senstivity figures- these exclude derived variables
base_model = model.base_model if isinstance(model,
fv3fit.DerivedModel) else model
try:
plot_jacobian(
base_model,
os.path.join(args.output_path,
"model_sensitivity_figures"), # type: ignore
)
except AttributeError:
try:
input_feature_indices = get_variable_indices(
data=batches[0], variables=base_model.input_variables)
plot_rf_feature_importance(
input_feature_indices,
base_model,
os.path.join(args.output_path, "model_sensitivity_figures"),
)
except AttributeError:
logger.info(
f"Base model is {type(base_model).__name__}, "
"which currently has no feature importance or Jacobian "
"calculation implemented.")
pass
mapper = _get_data_mapper_if_exists(config)
if mapper is not None:
snapshot_time = (args.snapshot_time or sorted(
config.kwargs.get("timesteps", list(mapper.keys())))[0])
snapshot_key = nearest_time(snapshot_time, list(mapper.keys()))
ds_snapshot = predict_function(mapper[snapshot_key])
vertical_vars = [
var for var in model.output_variables if is_3d(ds_snapshot[var])
]
ds_snapshot = insert_column_integrated_vars(ds_snapshot, vertical_vars)
predicted_vars = [
var for var in ds_snapshot if "derivation" in ds_snapshot[var].dims
]
# add snapshotted prediction to saved diags.nc
ds_diagnostics = ds_diagnostics.merge(
safe.get_variables(ds_snapshot, predicted_vars).rename(
{v: f"{v}_snapshot"
for v in predicted_vars}))
ds_transect = _get_transect(ds_snapshot, grid, vertical_vars)
_write_nc(ds_transect, args.output_path, TRANSECT_NC_NAME)
ds_diagnostics = _add_derived_diagnostics(ds_diagnostics)
_write_nc(
ds_diagnostics,
args.output_path,
DIAGS_NC_NAME,
)
# convert and output metrics json
metrics = _average_metrics_dict(ds_scalar_metrics)
with fsspec.open(os.path.join(args.output_path, METRICS_JSON_NAME),
"w") as f:
json.dump(metrics, f, indent=4)
metadata = {}
metadata["model_path"] = args.model_path
metadata["data_config"] = dataclasses.asdict(config)
with fsspec.open(os.path.join(args.output_path, METADATA_JSON_NAME),
"w") as f:
json.dump(metadata, f, indent=4)
logger.info(f"Finished processing dataset diagnostics and metrics.")
def __post_init__(self: "Adapter"):
self.model = fv3fit.load(self.config.url)