Loading README.md +66 −16 Original line number Diff line number Diff line Loading @@ -203,24 +203,74 @@ passed a ``Featurizer`` object. DeepChem provides a number of different subclasses of ``Featurizer`` for convenience: ### Performances 1. Index splitting |Dataset |N(tasks) |N(samples) |Model |Train score/ROC-AUC|Valid score/ROC-AUC|Time(loading)/s |Time(running)/s| |-----------|-----------|-----------|--------------------|-------------------|-------------------|----------------|---------------| |tox21 |12 |8014 |logistic regression |0.910 |0.759 |30 |30 | | | | |tensorflow(MT-NN) |0.987 |0.800 |30 |30 | | | | |graph convolution |0.930 |0.819 |40 |40 | |muv |17 |93127 |logistic regression |0.910 |0.744 |600 |800 | | | | |tensorflow(MT-NN) |0.980 |0.710 |600 |800 | | | | |graph convolution |0.881 |0.832 |800 |1200 | |pcba |128 |439863 |logistic regression |0.794 |0.762 |1800 |15000 | | | | |tensorflow(MT-NN) |0.949 |0.791 |1800 |15000 | | | | |graph convolution |0.866 |0.836 |2200 |20000 | |sider |27 |1427 |logistic regression |0.900 |0.620 |15 |40 | | | | |tensorflow(MT-NN) |0.931 |0.647 |15 |60 | | | | |graph convolution |0.845 |0.646 |20 |60 | |toxcast |617 |8615 |logistic regression |0.762 |0.622 |80 |2000 | | | | |tensorflow(MT-NN) |0.926 |0.705 |80 |2400 | | | | |graph convolution |0.906 |0.725 |80 |3000 | |tox21 |12 |8014 |logistic regression |0.910 |0.759 |30 |60 | | | | |tensorflow(MT-NN) |0.987 |0.800 |30 |60 | | | | |robust MT-NN |0.979 |0.741 |30 |90 | | | | |graph convolution |0.930 |0.819 |40 |160 | |muv |17 |93127 |logistic regression |0.910 |0.744 |600 |450 | | | | |tensorflow(MT-NN) |0.980 |0.710 |600 |400 | | | | |robust MT-NN |0.986 |0.672 |600 |550 | | | | |graph convolution |0.881 |0.832 |800 |1800 | |pcba |128 |439863 |logistic regression |0.794 |0.762 |1800 |10000 | | | | |tensorflow(MT-NN) |0.949 |0.791 |1800 |9000 | | | | |graph convolution |0.866 |0.836 |2200 |14000 | |sider |27 |1427 |logistic regression |0.900 |0.620 |15 |80 | | | | |tensorflow(MT-NN) |0.931 |0.647 |15 |75 | | | | |graph convolution |0.845 |0.646 |20 |50 | |toxcast |617 |8615 |logistic regression |0.762 |0.622 |80 |2600 | | | | |tensorflow(MT-NN) |0.926 |0.705 |80 |2300 | | | | |graph convolution |0.906 |0.725 |80 |900 | 2. Random splitting(N(tasks), N(samples), Time omitted) |Dataset |Model |Train score/ROC-AUC|Valid score/ROC-AUC| |-----------|--------------------|-------------------|-------------------| |tox21 |logistic regression |0.903 |0.741 | | |tensorflow(MT-NN) |0.846 |0.812 | | |robust MT-NN |0.844 |0.793 | | |graph convolution |0.872 |0.816 | |muv |logistic regression |0.961 |0.696 | | |tensorflow(MT-NN) |0.895 |0.740 | | |robust MT-NN |0.914 |0.667 | | |graph convolution |0.846 |0.776 | |pcba |logistic regression |0.807 |0.772 | | |tensorflow(MT-NN) |0.811 |0.787 | | |robust MT-NN |0.809 |0.778 | | |graph convolution |0.875 |0.844 | |sider |logistic regression |0.932 |0.628 | | |tensorflow(MT-NN) |0.779 |0.665 | | |robust MT-NN |0.761 |0.621 | | |graph convolution |0.706 |0.638 | |toxcast |logistic regression |0.737 |0.543 | | |tensorflow(MT-NN) |0.831 |0.684 | | |robust MT-NN |0.814 |0.692 | | |graph convolution |0.820 |0.692 | 3. Scaffold splitting(N(tasks), N(samples), Time omitted) |Dataset |Model |Train score/ROC-AUC|Valid score/ROC-AUC| |-----------|--------------------|-------------------|-------------------| |tox21 |logistic regression |0.900 |0.650 | | |tensorflow(MT-NN) |0.863 |0.703 | | |robust MT-NN |0.861 |0.710 | | |graph convolution |0.885 |0.732 | |muv |logistic regression |0.947 |0.767 | | |tensorflow(MT-NN) |0.899 |0.762 | | |robust MT-NN |0.944 |0.726 | | |graph convolution |0.872 |0.795 | |pcba |logistic regression |0.810 |0.742 | | |tensorflow(MT-NN) |0.814 |0.760 | | |robust MT-NN |0.812 |0.756 | | |graph convolution |0.874 |0.817 | |sider |logistic regression |0.926 |0.592 | | |tensorflow(MT-NN) |0.776 |0.557 | | |robust MT-NN |0.797 |0.560 | | |graph convolution |0.722 |0.583 | |toxcast |logistic regression |0.716 |0.492 | | |tensorflow(MT-NN) |0.828 |0.617 | | |robust MT-NN |0.830 |0.614 | | |graph convolution |0.832 |0.638 | ## Contributing to DeepChem Loading Loading
README.md +66 −16 Original line number Diff line number Diff line Loading @@ -203,24 +203,74 @@ passed a ``Featurizer`` object. DeepChem provides a number of different subclasses of ``Featurizer`` for convenience: ### Performances 1. Index splitting |Dataset |N(tasks) |N(samples) |Model |Train score/ROC-AUC|Valid score/ROC-AUC|Time(loading)/s |Time(running)/s| |-----------|-----------|-----------|--------------------|-------------------|-------------------|----------------|---------------| |tox21 |12 |8014 |logistic regression |0.910 |0.759 |30 |30 | | | | |tensorflow(MT-NN) |0.987 |0.800 |30 |30 | | | | |graph convolution |0.930 |0.819 |40 |40 | |muv |17 |93127 |logistic regression |0.910 |0.744 |600 |800 | | | | |tensorflow(MT-NN) |0.980 |0.710 |600 |800 | | | | |graph convolution |0.881 |0.832 |800 |1200 | |pcba |128 |439863 |logistic regression |0.794 |0.762 |1800 |15000 | | | | |tensorflow(MT-NN) |0.949 |0.791 |1800 |15000 | | | | |graph convolution |0.866 |0.836 |2200 |20000 | |sider |27 |1427 |logistic regression |0.900 |0.620 |15 |40 | | | | |tensorflow(MT-NN) |0.931 |0.647 |15 |60 | | | | |graph convolution |0.845 |0.646 |20 |60 | |toxcast |617 |8615 |logistic regression |0.762 |0.622 |80 |2000 | | | | |tensorflow(MT-NN) |0.926 |0.705 |80 |2400 | | | | |graph convolution |0.906 |0.725 |80 |3000 | |tox21 |12 |8014 |logistic regression |0.910 |0.759 |30 |60 | | | | |tensorflow(MT-NN) |0.987 |0.800 |30 |60 | | | | |robust MT-NN |0.979 |0.741 |30 |90 | | | | |graph convolution |0.930 |0.819 |40 |160 | |muv |17 |93127 |logistic regression |0.910 |0.744 |600 |450 | | | | |tensorflow(MT-NN) |0.980 |0.710 |600 |400 | | | | |robust MT-NN |0.986 |0.672 |600 |550 | | | | |graph convolution |0.881 |0.832 |800 |1800 | |pcba |128 |439863 |logistic regression |0.794 |0.762 |1800 |10000 | | | | |tensorflow(MT-NN) |0.949 |0.791 |1800 |9000 | | | | |graph convolution |0.866 |0.836 |2200 |14000 | |sider |27 |1427 |logistic regression |0.900 |0.620 |15 |80 | | | | |tensorflow(MT-NN) |0.931 |0.647 |15 |75 | | | | |graph convolution |0.845 |0.646 |20 |50 | |toxcast |617 |8615 |logistic regression |0.762 |0.622 |80 |2600 | | | | |tensorflow(MT-NN) |0.926 |0.705 |80 |2300 | | | | |graph convolution |0.906 |0.725 |80 |900 | 2. Random splitting(N(tasks), N(samples), Time omitted) |Dataset |Model |Train score/ROC-AUC|Valid score/ROC-AUC| |-----------|--------------------|-------------------|-------------------| |tox21 |logistic regression |0.903 |0.741 | | |tensorflow(MT-NN) |0.846 |0.812 | | |robust MT-NN |0.844 |0.793 | | |graph convolution |0.872 |0.816 | |muv |logistic regression |0.961 |0.696 | | |tensorflow(MT-NN) |0.895 |0.740 | | |robust MT-NN |0.914 |0.667 | | |graph convolution |0.846 |0.776 | |pcba |logistic regression |0.807 |0.772 | | |tensorflow(MT-NN) |0.811 |0.787 | | |robust MT-NN |0.809 |0.778 | | |graph convolution |0.875 |0.844 | |sider |logistic regression |0.932 |0.628 | | |tensorflow(MT-NN) |0.779 |0.665 | | |robust MT-NN |0.761 |0.621 | | |graph convolution |0.706 |0.638 | |toxcast |logistic regression |0.737 |0.543 | | |tensorflow(MT-NN) |0.831 |0.684 | | |robust MT-NN |0.814 |0.692 | | |graph convolution |0.820 |0.692 | 3. Scaffold splitting(N(tasks), N(samples), Time omitted) |Dataset |Model |Train score/ROC-AUC|Valid score/ROC-AUC| |-----------|--------------------|-------------------|-------------------| |tox21 |logistic regression |0.900 |0.650 | | |tensorflow(MT-NN) |0.863 |0.703 | | |robust MT-NN |0.861 |0.710 | | |graph convolution |0.885 |0.732 | |muv |logistic regression |0.947 |0.767 | | |tensorflow(MT-NN) |0.899 |0.762 | | |robust MT-NN |0.944 |0.726 | | |graph convolution |0.872 |0.795 | |pcba |logistic regression |0.810 |0.742 | | |tensorflow(MT-NN) |0.814 |0.760 | | |robust MT-NN |0.812 |0.756 | | |graph convolution |0.874 |0.817 | |sider |logistic regression |0.926 |0.592 | | |tensorflow(MT-NN) |0.776 |0.557 | | |robust MT-NN |0.797 |0.560 | | |graph convolution |0.722 |0.583 | |toxcast |logistic regression |0.716 |0.492 | | |tensorflow(MT-NN) |0.828 |0.617 | | |robust MT-NN |0.830 |0.614 | | |graph convolution |0.832 |0.638 | ## Contributing to DeepChem Loading
