def test_train_small_bootstrap_multi_target(small_moddata, tf_session):
"""Tests the multi-target training."""
from modnet.models import EnsembleMODNetModel
data = small_moddata
# set 'optimal' features manually
data.optimal_features = [
col for col in data.df_featurized.columns
if col.startswith("ElementProperty")
]
model = EnsembleMODNetModel(
[[["eform", "egap"]]],
weights={
"eform": 1,
"egap": 1
},
num_neurons=[[16], [8], [8], [4]],
n_feat=10,
n_models=3,
bootstrap=True,
)
model.fit(data, epochs=5)
model.predict(data, return_unc=True)
def test_train_small_bootstrap_single_target_classif(small_moddata,
tf_session):
"""Tests the single target training."""
from modnet.models import EnsembleMODNetModel
data = small_moddata
# set 'optimal' features manually
data.optimal_features = [
col for col in data.df_featurized.columns
if col.startswith("ElementProperty")
]
def is_metal(egap):
if egap == 0:
return 1
else:
return 0
data.df_targets["is_metal"] = data.df_targets["egap"].apply(is_metal)
model = EnsembleMODNetModel(
[[["is_metal"]]],
weights={"is_metal": 1},
num_neurons=[[16], [8], [8], [4]],
num_classes={"is_metal": 2},
n_feat=10,
n_models=3,
bootstrap=True,
)
model.fit(data, epochs=5)
model.predict(data)
model.predict(data, return_unc=True)