def test_qm9(self, run_config, builder_config):
job = Regression({
"name": "test",
"run_config": run_config,
"builder_config": builder_config
})
job.run()
def test_cosine_basis(self, run_config, builder_config):
job = Regression({
"name":
"test",
"run_config":
run_config,
"builder_config":
dict(**builder_config, basis_type="cosine"),
})
job.run()
def test_sum_points(self, run_config, builder_config):
job = Regression({
"name":
"test",
"run_config":
run_config,
"builder_config":
dict(**builder_config, sum_points=True),
})
job.run()
def test_non_residual(self, run_config, builder_config):
job = Regression({
"name":
"test",
"run_config":
run_config,
"builder_config":
dict(**builder_config, residual=False),
})
job.run()
def test_default_loads_eagerly(self, run_config, builder_config, model):
run_config["run_eagerly"] = True
builder_config["dynamic"] = True
job = Regression({
"name": "test",
"run_config": run_config,
"builder_config": builder_config
})
job.run()
model = load_model(model)
assert True
def test_iso17(self, run_config, builder_config):
loader_config = {"loader_type": "iso17_loader"}
job = Regression({
"name":
"test",
"run_config":
run_config,
"loader_config":
loader_config,
"builder_config":
dict(**builder_config, builder_type="force_builder"),
})
job.run()
def test_modified_qm9_vector_prediction_cartesian_output(
self, run_config, builder_config):
loader_config = {
"loader_type": "qm9_loader",
"load_kwargs": {
"modify_structures": True
},
}
job = Regression({
"name":
"test",
"run_config":
run_config,
"loader_config":
loader_config,
"builder_config":
dict(**builder_config, builder_type="cartesian_builder"),
})
job.run()
def test_basic_grid_search(self, run_config):
job = GridSearch(
job=Regression(exp_config={
"name": "test",
"run_config": run_config
}),
grid=self.GRID_CONFIG,
total_models=3,
)
job.run()
def test_single_dense_radial(self, run_config, builder_config):
job = Regression({
"name":
"test",
"run_config":
run_config,
"builder_config":
dict(
**builder_config, **{
"embedding_units": 32,
"model_num_layers": (3, 3, 3),
"si_units": 32,
"radial_factory": "single_dense",
"radial_kwargs": {
"num_layers": 1,
"units": 64,
"activation": "ssp",
"kernel_lambda": 0.01,
"bias_lambda": 0.01,
},
}),
})
job.run()
def test_regression_to_structure_prediction_to_cross_validation(
self, builder_config, run_config):
job = Pipeline(jobs=[
Regression(
exp_config={
"run_config":
run_config,
"loader_config": {
"loader_type": "iso17_loader"
},
"builder_config":
dict(**builder_config, builder_type="force_builder"),
}),
StructurePrediction(
exp_config={
"run_config":
run_config,
"loader_config": {
"loader_type": "qm9_loader",
"load_kwargs": {
"modify_structures": True
},
},
"builder_config":
dict(**builder_config, builder_type="cartesian_builder"),
}),
CrossValidate(
exp_config={
"run_config":
run_config,
"loader_config": {
"loader_type": "ts_loader",
"splitting": 5
},
"builder_config":
dict(**builder_config, builder_type="cartesian_builder"),
}),
])
job.run()
job = Pipeline(
exp_config={
"name": f"{Path(__file__).parent}",
"seed": 1
},
jobs=[
Regression(
exp_config={
"name": f"{Path(__file__).parent} QM9",
"seed": 1,
"run_config": {
"epochs": 50,
"test": False,
},
"loader_config": {
"loader_type": "qm9_loader",
"splitting": "90:10:0",
"map_points": False,
"load_kwargs": {
"custom_maxz": 36
},
},
"builder_config": {
"builder_type": "energy_builder"
},
}),
CrossValidate(
exp_config={
"name": f"{Path(__file__).parent} TS",
"seed": 1,
"run_config": {
"epochs": 1000,