Commit ce8dedb9 authored by miaecle's avatar miaecle
Browse files

temp save

parent 11cb66e2

README.md

+26 −1
Original line number Diff line number Diff line
@@ -220,6 +220,7 @@ Index splitting 
|           |MT-NN classification|0.934              |0.830              |

|           |Robust MT-NN        |0.949              |0.827              |

|           |Graph convolution   |0.946              |0.860              |

|           |DAG                 |0.953              |0.775              |

|hiv        |Logistic regression |0.864              |0.739              |

|           |Random forest       |0.999              |0.720              |

|           |XGBoost             |0.917              |0.745              |
@@ -251,6 +252,7 @@ Index splitting 
|           |MT-NN classification|0.856              |0.763              |

|           |Robust MT-NN        |0.857              |0.767              |

|           |Graph convolution   |0.872              |0.798              |

|           |DAG                 |0.831              |0.750              |

|toxcast    |Logistic regression |0.721              |0.575              |

|           |XGBoost             |0.738              |0.621              |

|           |MT-NN classification|0.830              |0.678              |
@@ -280,6 +282,7 @@ Random splitting 
|           |MT-NN classification|0.951              |0.834              |

|           |Robust MT-NN        |0.959              |0.830              |

|           |Graph convolution   |0.975              |0.876              |

|           |DAG                 |0.917              |0.744              |

|hiv        |Logistic regression |0.860              |0.806              |

|           |Random forest       |0.999              |0.850              |

|           |XGBoost             |0.933              |0.841              |
@@ -310,6 +313,7 @@ Random splitting 
|           |MT-NN classification|0.844              |0.795              |

|           |Robust MT-NN        |0.855              |0.773              |

|           |Graph convolution   |0.865              |0.827              |

|           |DAG                 |0.872              |0.758              |

|toxcast    |Logistic regression |0.725        	     |0.586              |

|           |XGBoost             |0.738              |0.633              |

|           |MT-NN classification|0.836        	     |0.684              |
@@ -339,6 +343,7 @@ Scaffold splitting 
|           |MT-NN classification|0.937              |0.828              |

|           |Robust MT-NN        |0.956              |0.821              |

|           |Graph convolution   |0.965              |0.900              |

|           |DAG                 |0.925              |0.703              |

|hiv        |Logistic regression |0.858              |0.798              |

|           |Random forest       |0.946              |0.562              |

|           |XGBoost             |0.927              |0.830              |
@@ -369,6 +374,7 @@ Scaffold splitting 
|           |MT-NN classification|0.863              |0.703              |

|           |Robust MT-NN        |0.861              |0.710              |

|           |Graph convolution   |0.885              |0.732              |

|           |DAG                 |0.861              |0.670              |

|toxcast    |Logistic regression |0.716              |0.492              |

|           |XGBoost             |0.741              |0.587              |

|           |MT-NN classification|0.828              |0.617              |
@@ -405,14 +411,17 @@ Scaffold splitting 
|                |XGBoost             |Index       |0.898         |0.664         |

|                |NN regression       |Index       |0.868         |0.578         |

|                |Graphconv regression|Index       |0.967         |0.790         |

|                |DAG regression      |Index       |0.921         |0.827         |

|                |Random forest       |Random      |0.951         |0.684         |

|                |XGBoost             |Random      |0.927         |0.727         |

|                |NN regression       |Random      |0.865         |0.574         |

|                |Graphconv regression|Random      |0.964         |0.782         |

|                |DAG regression      |Random      |0.898         |0.857         |

|                |Random forest       |Scaffold    |0.953         |0.284         |

|                |XGBoost             |Scaffold    |0.890         |0.316         |

|                |NN regression       |Scaffold    |0.866         |0.342         |

|                |Graphconv regression|Scaffold    |0.967         |0.606         |

|                |DAG regression      |Scaffold    |0.931         |0.647         |

|hopv            |Random forest       |Index       |0.943         |0.338         |

|                |MT-NN regression    |Index       |0.725         |0.293         |

|                |Graphconv regression|Index       |0.307         |0.284         |
@@ -426,12 +435,15 @@ Scaffold splitting 
|lipo            |Random forest       |Index       |0.960         |0.483         |

|                |NN regression       |Index       |0.825         |0.513         |

|                |Graphconv regression|Index       |0.865         |0.704         |

|                |DAG regression      |Index       |0.752         |0.507         |

|                |Random forest       |Random      |0.958         |0.518         |

|                |NN regression       |Random      |0.818         |0.445         |

|                |Graphconv regression|Random      |0.867         |0.722         |

|                |DAG regression      |Random      |0.751         |0.446         |

|                |Random forest       |Scaffold    |0.958         |0.329         |

|                |NN regression       |Scaffold    |0.831         |0.302         |

|                |Graphconv regression|Scaffold    |0.882         |0.593         |

|                |DAG regression      |Scaffold    |0.670         |0.378         |

|nci             |XGBoost             |Index       |0.441         |0.066         |

|                |MT-NN regression    |Index       |0.690         |0.062         |

|                |Graphconv regression|Index       |0.123         |0.053         |
@@ -450,21 +462,33 @@ Scaffold splitting 
|ppb             |Random forest       |Index       |0.951         |0.235         |

|                |NN regression       |Index       |0.902         |0.333         |

|                |Graphconv regression|Index       |0.673         |0.442         |

|                |DAG regression      |Index       |0.516         |0.295         |

|                |Random forest       |Random      |0.950         |0.220         |

|                |NN regression       |Random      |0.903         |0.244         |

|                |Graphconv regression|Random      |0.646         |0.429         |

|                |DAG regression      |Random      |0.571         |0.227         |

|                |Random forest       |Scaffold    |0.943         |0.176         |

|                |NN regression       |Scaffold    |0.902         |0.144         |

|                |Graphconv regression|Scaffold    |0.695         |0.391         |

|                |DAG regression      |Scaffold    |0.632         |0.272         |

|qm7             |NN regression       |Index       |0.997         |0.992         |

|                |DTNN                |Index       |0.998         |0.996         |

|                |NN regression       |Random      |0.998         |0.997         |

|                |DTNN                |Random      |0.998         |0.998         |

|                |NN regression       |Stratified  |0.998         |0.997         | 

|                |DTNN                |Stratified  |0.998         |0.998         | 

|qm7b            |MT-NN regression    |Index       |0.903         |0.789         |

|                |DTNN                |Index       |0.872         |0.821         |

|                |MT-NN regression    |Random      |0.893         |0.839         |

|                |DTNN                |Random      |0.865         |0.849         |

|                |MT-NN regression    |Stratified  |0.891         |0.859         | 

|                |DTNN                |Stratified  |0.853         |0.839         | 

|qm8             |MT-NN regression    |Index       |0.783         |0.656         |

|                |DTNN                |Index       |0.737         |0.639         |

|                |MT-NN regression    |Random      |0.747         |0.660         |

|                |DTNN                |Random      |0.731         |0.711         |

|                |MT-NN regression    |Stratified  |0.756         |0.681         |

|                |DTNN                |Stratified  |0.714         |0.683         | 

|qm9             |MT-NN regression    |Index       |0.733         |0.766         |

|                |MT-NN regression    |Random      |0.852         |0.833         |

|                |MT-NN regression    |Stratified  |0.764         |0.792         |
@@ -472,6 +496,7 @@ Scaffold splitting 
|                |XGBoost             |Index       |0.884         |0.784         |

|                |NN regression       |Index       |0.917         |0.764         |

|                |Graphconv regression|Index       |0.982         |0.903         |

|                |DAG regression      |Index       |0.891         |0.777         | 

|                |Random forest       |Random      |0.967         |0.752         |

|                |XGBoost             |Random      |0.906         |0.745         |

|                |NN regression       |Random      |0.908         |0.711         |

deepchem/molnet/run_benchmark_models.py

+2 −2
Original line number Diff line number Diff line
@@ -278,7 +278,7 @@ def benchmark_classification(train_dataset, 
    graph_model.add(deepchem.nn.BatchNormalization(epsilon=1e-5, mode=1))

    graph_model.add(

        deepchem.nn.WeaveGather(

            batch_size, n_input=n_graph_feat, gaussian_expand=False))

            batch_size, n_input=n_graph_feat, gaussian_expand=True))



    model = deepchem.models.MultitaskGraphClassifier(

        graph_model,
@@ -600,7 +600,7 @@ def benchmark_regression(train_dataset, 
    graph_model.add(deepchem.nn.BatchNormalization(epsilon=1e-5, mode=1))

    graph_model.add(

        deepchem.nn.WeaveGather(

            batch_size, n_input=n_graph_feat, gaussian_expand=False))

            batch_size, n_input=n_graph_feat, gaussian_expand=True))



    model = deepchem.models.MultitaskGraphRegressor(

        graph_model,

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