def predict(self, smiles, average=True):
if self.feature_type == 'fingerprints':
fps = get_fp(smiles)
assert len(smiles) == len(fps)
clean_smiles = []
clean_fps = []
nan_smiles = []
for i in range(len(fps)):
if np.isnan(sum(fps[i])):
nan_smiles.append(smiles[i])
else:
clean_smiles.append(smiles[i])
clean_fps.append(fps[i])
clean_fps = np.array(clean_fps)
elif self.feature_type == 'descriptors':
clean_fps, clean_smiles, nan_smiles = get_desc(smiles, self.calc)
prediction = []
if len(clean_fps) > 0:
for i in range(self.n_ensemble):
m = self.model[i]
if self.feature_type == 'descriptors':
clean_fps, _ = normalize_desc(clean_fps, self.desc_mean[i])
prediction.append(m.predict(clean_fps))
prediction = np.array(prediction)
if average:
prediction = prediction.mean(axis=0)
assert len(clean_smiles) == len(prediction)
return clean_smiles, prediction, nan_smiles
def fit_model(self, data, cross_val_data, cross_val_labels):
eval_metrics = []
for i in range(self.n_ensemble):
train_sm = np.concatenate(cross_val_data[:i] + cross_val_data[(i + 1):])
test_sm = cross_val_data[i]
train_labels = np.concatenate(cross_val_labels[:i] + cross_val_labels[(i + 1):])
test_labels = cross_val_labels[i]
fp_train = get_fp(train_sm)
fp_test = get_fp(test_sm)
self.model[i].fit(fp_train, train_labels.ravel())
predicted = self.model[i].predict(fp_test)
if self.model_type == 'classifier':
fpr, tpr, thresholds = metrics.roc_curve(test_labels, predicted)
eval_metrics.append(metrics.auc(fpr, tpr))
metrics_type = 'AUC'
elif self.model_type == 'regressor':
r2 = metrics.r2_score(test_labels, predicted)
eval_metrics.append(r2)
metrics_type = 'R^2 score'
return eval_metrics, metrics_type
def predict(self, smiles, average=True):
fps = get_fp(smiles)
assert len(smiles) == len(fps)
clean_smiles = []
clean_fps = []
nan_smiles = []
for i in range(len(fps)):
if np.isnan(sum(fps[i])):
nan_smiles.append(smiles[i])
else:
clean_smiles.append(smiles[i])
clean_fps.append(fps[i])
clean_fps = np.array(clean_fps)
prediction = []
if len(clean_fps) > 0:
for m in self.model:
prediction.append(m.predict(clean_fps))
prediction = np.array(prediction)
if average:
prediction = prediction.mean(axis=0)
assert len(clean_smiles) == len(prediction)
return clean_smiles, prediction, nan_smiles
def fit_model(self, data):
eval_metrics = []
if self.feature_type == 'fingerprints':
fps = get_fp(data.smiles)
elif self.feature_type == 'descriptors':
fps, _, _ = get_desc(data.smiles, self.calc)
if self.model_type == 'classifier':
cross_val_data, cross_val_labels = \
cross_validation_split(fps, data.binary_labels)
elif self.model_type == 'regressor':
cross_val_data, cross_val_labels = \
cross_validation_split(fps, data.property)
for i in range(self.n_ensemble):
train_sm = np.concatenate(cross_val_data[:i] +
cross_val_data[(i + 1):])
test_sm = cross_val_data[i]
train_labels = np.concatenate(cross_val_labels[:i] +
cross_val_labels[(i + 1):])
test_labels = cross_val_labels[i]
if self.feature_type == 'descriptors':
train_sm, desc_mean = normalize_desc(train_sm)
self.desc_mean[i] = desc_mean
test_sm, _ = normalize_desc(test_sm, desc_mean)
self.model[i].fit(train_sm, train_labels.ravel())
predicted = self.model[i].predict(test_sm)
if self.model_type == 'classifier':
fpr, tpr, thresholds = metrics.roc_curve(
test_labels, predicted)
eval_metrics.append(metrics.auc(fpr, tpr))
metrics_type = 'AUC'
elif self.model_type == 'regressor':
r2 = metrics.r2_score(test_labels, predicted)
eval_metrics.append(r2)
metrics_type = 'R^2 score'
return eval_metrics, metrics_type