将Deepchem磁盘数据存储到numpy数组

时间:2019-10-09 01:33:57

标签: python numpy deep-learning rdkit

我正在为Deepchem模型使用GraphConvolution包装器,如下所示。我在.csv中有我的微笑数据,该数据由5个分子组成,具有微笑表示和各自的活动。可以直接从here访问数据。

导入库:

from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import tensorflow as tf
import deepchem as dc
from deepchem.models.tensorgraph.models.graph_models import GraphConvModel

加载数据并对其进行特征化,使其适合图卷积。

graph_featurizer = dc.feat.graph_features.ConvMolFeaturizer()
loader_train = dc.data.data_loader.CSVLoader( tasks=['Activity'], smiles_field="smiles",featurizer=graph_featurizer)
dataset_train = loader_train.featurize( './train_smiles_data.csv')

分析已加载和特征化的数据(我的尝试)

dataset_train.X

array([<deepchem.feat.mol_graphs.ConvMol object at 0x7f8bfc3ad748>,
       <deepchem.feat.mol_graphs.ConvMol object at 0x7f8bfc367828>,
       <deepchem.feat.mol_graphs.ConvMol object at 0x7f8bfc367208>,
       <deepchem.feat.mol_graphs.ConvMol object at 0x7f8bfc369c50>],
      dtype=object)


dataset_train.y

array([[2.71],
       [4.41],
       [3.77],
       [4.2 ]])

for x, y, w, id in dataset_train.itersamples():
    print(x, y, w, id)

<deepchem.feat.mol_graphs.ConvMol object at 0x7f8bfc3ad6a0> [2.71] [1.] CC1=C(O)C=CC=C1
<deepchem.feat.mol_graphs.ConvMol object at 0x7f8bfc30f518> [4.41] [1.] [O-][N+](=O)C1=CC=C(Br)S1
<deepchem.feat.mol_graphs.ConvMol object at 0x7f8bfc30f748> [3.77] [1.] CCC/C=C/C=O
<deepchem.feat.mol_graphs.ConvMol object at 0x7f8bfc30f940> [4.2] [1.] CCCCCC1=CC=CS1

我想要什么?

从上面的代码看来,dataset_train.X给出了diskobject之类的<deepchem.feat.mol_graphs.ConvMol object at 0x7f8bfc3ad6a0>,而不是numpy array之类的dataset_train.y

我怎么知道dataset_train.X中存储了什么类型的数据?如何查看存储在dataset_train.X中的数据?换句话说,如何将dataset_train.X转换成可以检查其中数据的格式?

我相信应该有一些方法可以做到这一点。

1 个答案:

答案 0 :(得分:1)

根据您的previous question dataset_train.X是ConvMol对象的数组。这些ConvMol对象是每个输入分子的特征的容器。这些特征没有像您的目标“ train_dataset.y”那样被表示,因为它们是更复杂的图形特征。再次查看ConvMol对象的source code和ConvMolFeaturizer的source code。然后,您可以确定如何解释这些功能:

# Inspect features for molecule 0
conv_feature = dataset_train.X[0]
# Print the atom features
print(conv_feature.get_atom_features())
# Print the adjacency list
print(conv_feature.get_adjancency_list())