def _check_datasets(self, dataset=None, csv_dataset=None):
if csv_dataset:
if dataset:
print(
"Dataset and csv.dataset are given, hence dataset will be overwritten by csv.data."
)
dataset = DataSet.read_csv(csv_dataset, index_col=None)
return dataset
def test_train_experimental_emulator():
model_name = f"reizman_suzuki_case_1"
domain = ReizmanSuzukiEmulator.setup_domain()
ds = DataSet.read_csv(DATA_PATH / f"{model_name}.csv")
exp = ExperimentalEmulator(model_name,
domain,
dataset=ds,
regressor=ANNRegressor)
# Test grid search cross validation and training
# params = {
# "regressor__net__max_epochs": [1, 1000],
# }
params = None
exp.train(cv_folds=5,
max_epochs=1000,
random_state=100,
search_params=params,
verbose=0)
# Testing
res = exp.test()
r2 = res["test_r2"].mean()
assert r2 > 0.8
# Test plotting
fig, ax = exp.parity_plot(output_variables="yld", include_test=True)
# Test saving/loading
exp.save("test_ee")
exp_2 = ExperimentalEmulator.load(model_name, "test_ee")
assert all(exp.descriptors_features) == all(exp_2.descriptors_features)
assert exp.n_examples == exp_2.n_examples
assert all(exp.output_variable_names) == all(exp_2.output_variable_names)
assert exp.clip == exp_2.clip
exp_2.X_train, exp_2.y_train, exp_2.X_test, exp_2.y_test = (
exp.X_train,
exp.y_train,
exp.X_test,
exp.y_test,
)
res = exp_2.test(X_test=exp.X_test, y_test=exp.y_test)
exp.parity_plot(output_variables="yld", include_test=True)
r2 = res["test_r2"].mean()
assert r2 > 0.8
shutil.rmtree("test_ee")
def _train_baumgartner(use_descriptors=False,
show_plots=False,
save_plots=True):
# Setup
model_name = f"baumgartner_aniline_cn_crosscoupling"
domain = BaumgartnerCrossCouplingEmulator.setup_domain()
ds = DataSet.read_csv(DATA_PATH / f"{model_name}.csv")
# Create emulator and train
model_name += "_descriptors" if use_descriptors else ""
exp = ExperimentalEmulator(
model_name,
domain,
dataset=ds,
regressor=ANNRegressor,
output_variable_names=["yield"],
descriptors_features=["catalyst", "base"] if use_descriptors else [],
)
res = exp.train(max_epochs=MAX_EPOCHS,
cv_folds=CV_FOLDS,
random_state=100,
test_size=0.2)
# Run test
res_test = exp.test()
res.update(res_test)
# Save emulator
model_path = pathlib.Path(MODELS_PATH / model_name)
model_path.mkdir(exist_ok=True)
exp.save(model_path)
# Make plot for posteriority sake
fig, ax = exp.parity_plot(include_test=True)
if save_plots:
fig.savefig(f"results/{model_name}.png", dpi=100)
if show_plots:
plt.show()
return res
results_average = [{
f"avg_{score_name}": scores.mean()
for score_name, scores in result.items()
} for result in results]
index = [f"case_{i}" for i in range(1, 5)]
results_df = pd.DataFrame.from_records(results_average, index=index)
results_df.index.rename("case", inplace=True)
results_df.to_csv(f"results/reizman_suzuki_scores.csv")
def train_one_reizman(case, show_plots=False, save_plots=True):
# Setup
model_name = f"reizman_suzuki_case_{case}"
domain = ReizmanSuzukiEmulator.setup_domain()
ds = DataSet.read_csv(DATA_PATH / f"{model_name}.csv")
# Create emulator and train
exp = ExperimentalEmulator(
model_name,
domain,
dataset=ds,
regressor=ANNRegressor,
)
res = exp.train(max_epochs=MAX_EPOCHS,
cv_folds=CV_FOLDS,
random_state=100,
test_size=0.2)
# Run test
res_test = exp.test()
