分子グラフの畳み込みモデルでdelaney-水溶解度データセットの回帰を行います。
csvデータのロード
例のごとくdelaney水溶解度データセットをロードします。 MPNNモデルやCoulomb matrix featurizerなどを使いたい場合は立体構造が必要ですが、ConvMolFeaturizerはsmiles構造でOKです。
import deepchem as dc from rdkit import Chem import numpy as np csv = "datasets/delaney-processed.csv" featurizer = dc.feat.ConvMolFeaturizer() loader = dc.data.CSVLoader( tasks=['measured log solubility in mols per litre'], smiles_field="smiles", featurizer=featurizer) dataset = loader.featurize(csv)
>>>dataset.X.shape (1128, 1024) >>>dataset.y.shape (1128, 1)
データセットの分割
splitter = dc.splits.ScaffoldSplitter() train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split(dataset,frac_train=0.8, frac_valid=0.1,frac_test=0.1) transformers = [dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset)] for transformer in transformers: train_dataset = transformer.transform(train_dataset) valid_dataset = transformer.transform(valid_dataset) test_dataset = transformer.transform(test_dataset)
モデルの訓練
from deepchem.models.tensorgraph.models.graph_models import GraphConvModel model = GraphConvModel(n_tasks=1, batch_size=64, uncertainty=False, mode='regression') model.fit(train_dataset, nb_epoch=30)
精度の評価
metric = dc.metrics.Metric(dc.metrics.r2_score) print("Evaluating model") train_scores = model.evaluate(train_dataset, [metric], transformers) valid_scores = model.evaluate(valid_dataset, [metric], transformers) print("Train scores") print(train_scores) print("Validation scores") print(valid_scores)
### out
Evaluating model
computed_metrics: [0.9236492543579347]
computed_metrics: [0.5783662081038962]
Train scores
{'r2_score': 0.9236492543579347}
Validation scores
{'r2_score': 0.5783662081038962}
可視化
まあまあの精度でしょうか。
import matplotlib.pyplot as plt %matplotlib inline predicted_test = model.predict(test_dataset) true_test = test_dataset.y plt.scatter(predicted_test, true_test) plt.xlabel('Predicted log-solubility in mols/liter') plt.ylabel('True log-solubility in mols/liter') plt.title('GCNN-predicted vs. true log-solubilities')
