Merge branch 'jenkins' of github.com:lilleswing/deepchem into jenkins

|Dataset    |Model               |Train score/ROC-AUC|Valid score/ROC-AUC|

|-----------|--------------------|-------------------|-------------------|

|tox21      |logistic regression |0.903              |0.735              |

|           |Multitask network   |0.856              |0.783              |

|tox21      |logistic regression |0.902              |0.715              |

|           |Multitask network   |0.844              |0.795              |

|           |robust MT-NN        |0.855              |0.773              |

|           |graph convolution   |0.865              |0.827              |

|muv        |logistic regression |0.957              |0.719              |

      else:

        scaffolds[scaffold].append(ind)

    # Sort from largest to smallest scaffold sets

    scaffolds = {key: sorted(value) for key, value in scaffolds.items()}

    scaffold_sets = [scaffold_set for (scaffold, scaffold_set) in

                     sorted(scaffolds.items(), key=lambda x: -len(x[1]))]

                     sorted(scaffolds.items(), key=lambda x: (len(x[1]), x[1][0]), reverse=True)]

    train_cutoff = frac_train * len(dataset)

    valid_cutoff = (frac_train + frac_valid) * len(dataset)

    train_inds, valid_inds, test_inds = [], [], []

Index splitting,clintox,Multitask network,0.934,0.830

Index splitting,clintox,robust MT-NN,0.949,0.827

Index splitting,clintox,graph convolution,0.946,0.860

Random splitting,tox21,logistic regression,0.903,0.735

Random splitting,tox21,Multitask network,0.856,0.783

Random splitting,tox21,logistic regression,0.902,0.715

Random splitting,tox21,Multitask network,0.844,0.795

Random splitting,tox21,robust MT-NN,0.855,0.773

Random splitting,tox21,graph convolution,0.865,0.827

Random splitting,muv,logistic regression,0.957,0.719

source deactivate

conda remove --name $envname --all

rm results.csv

 No newline at end of file

rm examples/results.csv