def test_mpnn_regression_model(self):
tasks, dataset, transformers, metric = self.get_dataset(
'regression', 'Weave')
model = MPNNModel(len(tasks),
mode='regression',
n_hidden=75,
n_atom_feat=75,
n_pair_feat=14,
T=1,
M=1)
model.fit(dataset, nb_epoch=50)
scores = model.evaluate(dataset, [metric], transformers)
assert all(s < 0.1 for s in scores['mean_absolute_error'])
model.save()
model = TensorGraph.load_from_dir(model.model_dir)
scores2 = model.evaluate(dataset, [metric], transformers)
assert np.allclose(scores['mean_absolute_error'],
scores2['mean_absolute_error'])
def test_mpnn_model(self):
tasks, dataset, transformers, metric = self.get_dataset(
'classification', 'Weave')
model = MPNNModel(len(tasks),
mode='classification',
n_hidden=75,
n_atom_feat=75,
n_pair_feat=14,
T=1,
M=1)
model.fit(dataset, nb_epoch=20)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean-roc_auc_score'] >= 0.9
model.save()
model = TensorGraph.load_from_dir(model.model_dir)
scores2 = model.evaluate(dataset, [metric], transformers)
assert np.allclose(scores['mean-roc_auc_score'],
scores2['mean-roc_auc_score'])
def test_mpnn_regression_model(self):
tasks, dataset, transformers, metric = self.get_dataset(
'regression', 'Weave')
model = MPNNModel(
len(tasks),
mode='regression',
n_hidden=75,
n_atom_feat=75,
n_pair_feat=14,
T=1,
M=1)
model.fit(dataset, nb_epoch=50)
scores = model.evaluate(dataset, [metric], transformers)
assert all(s < 0.1 for s in scores['mean_absolute_error'])
model.save()
model = TensorGraph.load_from_dir(model.model_dir)
scores2 = model.evaluate(dataset, [metric], transformers)
assert np.allclose(scores['mean_absolute_error'],
scores2['mean_absolute_error'])
def test_mpnn_model(self):
tasks, dataset, transformers, metric = self.get_dataset(
'classification', 'Weave')
model = MPNNModel(
len(tasks),
mode='classification',
n_hidden=75,
n_atom_feat=75,
n_pair_feat=14,
T=1,
M=1)
model.fit(dataset, nb_epoch=20)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean-roc_auc_score'] >= 0.9
model.save()
model = TensorGraph.load_from_dir(model.model_dir)
scores2 = model.evaluate(dataset, [metric], transformers)
assert np.allclose(scores['mean-roc_auc_score'],
scores2['mean-roc_auc_score'])
def test_mpnn_regression_model(self):
tasks, dataset, transformers, metric = self.get_dataset(
'regression', 'Weave')
model = MPNNModel(len(tasks),
mode='regression',
n_hidden=75,
n_atom_feat=75,
n_pair_feat=14,
T=1,
M=1)
model.fit(dataset, nb_epoch=50)
scores = model.evaluate(dataset, [metric], transformers)
assert all(s < 0.1 for s in scores['mean_absolute_error'])
def test_mpnn_model(self):
tasks, dataset, transformers, metric = self.get_dataset(
'classification', 'Weave')
model = MPNNModel(len(tasks),
mode='classification',
n_hidden=75,
n_atom_feat=75,
n_pair_feat=14,
T=1,
M=1)
model.fit(dataset, nb_epoch=20)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean-roc_auc_score'] >= 0.9
def test_mpnn_regression_model():
tasks, dataset, transformers, metric = get_dataset('regression', 'Weave')
batch_size = 10
model = MPNNModel(len(tasks),
mode='regression',
n_hidden=75,
n_atom_feat=75,
n_pair_feat=14,
T=1,
M=1,
batch_size=batch_size)
model.fit(dataset, nb_epoch=60)
scores = model.evaluate(dataset, [metric], transformers)
assert scores['mean_absolute_error'] < 0.1
n_hidden = 100
T = 3
M = 5
nb_epoch = 10
model = MPNNModel(n_tasks = n_tasks, n_atom_feat = n_atom_feat, n_pair_feat = n_pair_feat,
n_hidden = n_hidden, T = T, M = M,
mode = "regression",
batch_size=batch_size, learning_rate=0.0001,
model_dir="/home/rod/Dropbox/Quimica/Analysis/ANalisis/Borradores/MPNNModel/") #To prevent overfitting
# Fit trained model
print("Fitting model")
model.fit(train_dataset, nb_epoch=nb_epoch)
model.save()
print("Evaluating model")
train_scores = model.evaluate(train_dataset, [metric])
valid_scores = model.evaluate(valid_dataset, [metric])
print("Train scores")
print(train_scores)
print("Validation scores")
print(valid_scores)
"""
With featurizer = dc.feat.ConvMolFeaturizer()
----------------------------------------
Train scores
{'mean-pearson_r2_score': 0.9637847589740351}
Validation scores
{'mean-pearson_r2_score': 0.8422518074477224}