dmlc--dgl
Mufei Li
331337fe5f
* Make dataset framework agnostic * Update * Update * Fix * Remove unused import
87 行
3.1 KiB
Python
87 行
3.1 KiB
Python
import numpy as np
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import sys
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from .csv_dataset import CSVDataset
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from .utils import smile_to_bigraph
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from ..utils import get_download_dir, download, _get_dgl_url
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try:
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import pandas as pd
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except ImportError:
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pass
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class Tox21(CSVDataset):
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_url = 'dataset/tox21.csv.gz'
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"""Tox21 dataset.
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The Toxicology in the 21st Century (https://tripod.nih.gov/tox21/challenge/)
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initiative created a public database measuring toxicity of compounds, which
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has been used in the 2014 Tox21 Data Challenge. The dataset contains qualitative
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toxicity measurements for 8014 compounds on 12 different targets, including nuclear
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receptors and stress response pathways. Each target results in a binary label.
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A common issue for multi-task prediction is that some datapoints are not labeled for
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all tasks. This is also the case for Tox21. In data pre-processing, we set non-existing
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labels to be 0 so that they can be placed in tensors and used for masking in loss computation.
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See examples below for more details.
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All molecules are converted into DGLGraphs. After the first-time construction,
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the DGLGraphs will be saved for reloading so that we do not need to reconstruct them everytime.
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Parameters
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----------
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smile_to_graph: callable, str -> DGLGraph
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A function turns smiles into a DGLGraph. Default one can be found
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at python/dgl/data/chem/utils.py named with smile_to_bigraph.
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"""
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def __init__(self, smile_to_graph=smile_to_bigraph):
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if 'pandas' not in sys.modules:
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from ...base import dgl_warning
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dgl_warning("Please install pandas")
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data_path = get_download_dir() + '/tox21.csv.gz'
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download(_get_dgl_url(self._url), path=data_path)
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df = pd.read_csv(data_path)
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self.id = df['mol_id']
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df = df.drop(columns=['mol_id'])
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super().__init__(df, smile_to_graph, cache_file_path="tox21_dglgraph.pkl")
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self._weight_balancing()
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def _weight_balancing(self):
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"""Perform re-balancing for each task.
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It's quite common that the number of positive samples and the
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number of negative samples are significantly different. To compensate
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for the class imbalance issue, we can weight each datapoint in
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loss computation.
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In particular, for each task we will set the weight of negative samples
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to be 1 and the weight of positive samples to be the number of negative
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samples divided by the number of positive samples.
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If weight balancing is performed, one attribute will be affected:
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* self._task_pos_weights is set, which is a list of positive sample weights
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for each task.
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"""
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num_pos = np.sum(self.labels, axis=0)
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num_indices = np.sum(self.mask, axis=0)
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self._task_pos_weights = (num_indices - num_pos) / num_pos
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@property
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def task_pos_weights(self):
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"""Get weights for positive samples on each task
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Returns
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-------
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list
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numpy array gives the weight of positive samples on all tasks
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"""
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return self._task_pos_weights
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