Loading README.md +26 −7 Original line number Diff line number Diff line Loading @@ -327,17 +327,22 @@ Scaffold splitting |chembl |MT-NN regression |Index |0.443 |0.427 | | |MT-NN regression |Random |0.464 |0.434 | | |MT-NN regression |Scaffold |0.484 |0.361 | |qm7 |MT-NN regression |Index |0.994 |0.010 | | |MT-NN regression |Random |0.860 |0.773 | | |MT-NN regression |User-defined|0.996 |0.996 | |qm7 |MT-NN regression |Index |0.994 |0.969 | | |MT-NN regression |Random |0.995 |0.992 | | |MT-NN regression |Stratified |0.992 |0.992 | |qm7b |MT-NN regression |Index |0.883 |0.785 | | |MT-NN regression |Random |0.864 |0.838 | | |MT-NN regression |Stratified |0.871 |0.847 | |kaggle |MT-NN regression |User-defined|0.748 |0.452 | |Dataset |Model |Splitting |Train score/MAE(kcal/mol)|Valid score/MAE(kcal/mol)| |----------------|--------------------|------------|-------------------------|-------------------------| |qm7 |MT-NN regression |Index |18.3 |172.0 | | |MT-NN regression |Random |44.3 |59.1 | |qm7 |MT-NN regression |Index |22.1 |23.2 | | |MT-NN regression |Random |16.2 |17.7 | | |MT-NN regression |Stratified |20.5 |20.8 | | |MT-NN regression |User-defined|9.0 |9.5 | * General features Number of tasks and examples in the datasets Loading @@ -358,7 +363,8 @@ Number of tasks and examples in the datasets |pdbbind(refined)|1 |3706 | |pdbbind(full) |1 |11908 | |chembl(5thresh) |691 |23871 | |gdb7 |1 |7165 | |qm7 |1 |7165 | |qm7b |14 |7211 | Loading @@ -369,6 +375,8 @@ Time needed for benchmark test(~20h in total) |tox21 |logistic regression |30 |60 | | |Multitask network |30 |60 | | |robust MT-NN |30 |90 | | |random forest |30 |6000 | | |IRV |30 |650 | | |graph convolution |40 |160 | |muv |logistic regression |600 |450 | | |Multitask network |600 |400 | Loading @@ -381,22 +389,33 @@ Time needed for benchmark test(~20h in total) |sider |logistic regression |15 |80 | | |Multitask network |15 |75 | | |robust MT-NN |15 |150 | | |random forest |15 |2200 | | |IRV |15 |150 | | |graph convolution |20 |50 | |toxcast |logistic regression |80 |2600 | | |Multitask network |80 |2300 | | |robust MT-NN |80 |4000 | | |graph convolution |80 |900 | |clintox |logistic regression |15 |10 | | |Multitask network |15 |20 | | |robust MT-NN |15 |30 | | |random forest |15 |200 | | |IRV |15 |10 | | |graph convolution |20 |130 | |delaney |MT-NN regression |10 |40 | | |graphconv regression|10 |40 | | |random forest |10 |30 | |sampl |MT-NN regression |10 |30 | | |graphconv regression|10 |40 | | |random forest |10 |20 | |nci |MT-NN regression |400 |1200 | | |graphconv regression|400 |2500 | |pdbbind(core) |MT-NN regression |0(featurized) |30 | |pdbbind(refined)|MT-NN regression |0(featurized) |40 | |pdbbind(full) |MT-NN regression |0(featurized) |60 | |chembl |MT-NN regression |200 |9000 | |gdb7 |MT-NN regression |10 |110 | |qm7 |MT-NN regression |10 |400 | |qm7b |MT-NN regression |10 |600 | |kaggle |MT-NN regression |2200 |3200 | Loading deepchem/trans/transformers.py +34 −0 Original line number Diff line number Diff line Loading @@ -633,6 +633,25 @@ class IRVTransformer(): self.transform_y = transform_y def realize(self, similarity, y, w): """find samples with top ten similarity values in the reference dataset Parameters: ----------- similarity: np.ndarray similarity value between target dataset and reference dataset should have size of (n_samples_in_target, n_samples_in_reference) y: np.array labels for a single task w: np.array weights for a single task Return: ---------- features: list n_samples * np.array of size (2*K,) each array includes K similarity values and corresponding labels """ features = [] similarity_xs = similarity * np.sign(w) for similarity_x in similarity_xs: Loading @@ -646,6 +665,21 @@ class IRVTransformer(): return features def X_transform(self, X_target): """ Calculate similarity between target dataset(X_target) and reference dataset(X): #(1 in intersection)/#(1 in union) similarity = (X_target ∩ X)/(X_target U X) Parameters: ----------- X_target: np.ndarray fingerprints of target dataset should have same length with X in the second axis Returns: ---------- X_target: np.ndarray features of size(batch_size, 2*K*n_tasks) """ X_target2 = [] n_features = X_target.shape[1] similarity = np.matmul(X_target, np.transpose(self.X)) / ( Loading Loading
README.md +26 −7 Original line number Diff line number Diff line Loading @@ -327,17 +327,22 @@ Scaffold splitting |chembl |MT-NN regression |Index |0.443 |0.427 | | |MT-NN regression |Random |0.464 |0.434 | | |MT-NN regression |Scaffold |0.484 |0.361 | |qm7 |MT-NN regression |Index |0.994 |0.010 | | |MT-NN regression |Random |0.860 |0.773 | | |MT-NN regression |User-defined|0.996 |0.996 | |qm7 |MT-NN regression |Index |0.994 |0.969 | | |MT-NN regression |Random |0.995 |0.992 | | |MT-NN regression |Stratified |0.992 |0.992 | |qm7b |MT-NN regression |Index |0.883 |0.785 | | |MT-NN regression |Random |0.864 |0.838 | | |MT-NN regression |Stratified |0.871 |0.847 | |kaggle |MT-NN regression |User-defined|0.748 |0.452 | |Dataset |Model |Splitting |Train score/MAE(kcal/mol)|Valid score/MAE(kcal/mol)| |----------------|--------------------|------------|-------------------------|-------------------------| |qm7 |MT-NN regression |Index |18.3 |172.0 | | |MT-NN regression |Random |44.3 |59.1 | |qm7 |MT-NN regression |Index |22.1 |23.2 | | |MT-NN regression |Random |16.2 |17.7 | | |MT-NN regression |Stratified |20.5 |20.8 | | |MT-NN regression |User-defined|9.0 |9.5 | * General features Number of tasks and examples in the datasets Loading @@ -358,7 +363,8 @@ Number of tasks and examples in the datasets |pdbbind(refined)|1 |3706 | |pdbbind(full) |1 |11908 | |chembl(5thresh) |691 |23871 | |gdb7 |1 |7165 | |qm7 |1 |7165 | |qm7b |14 |7211 | Loading @@ -369,6 +375,8 @@ Time needed for benchmark test(~20h in total) |tox21 |logistic regression |30 |60 | | |Multitask network |30 |60 | | |robust MT-NN |30 |90 | | |random forest |30 |6000 | | |IRV |30 |650 | | |graph convolution |40 |160 | |muv |logistic regression |600 |450 | | |Multitask network |600 |400 | Loading @@ -381,22 +389,33 @@ Time needed for benchmark test(~20h in total) |sider |logistic regression |15 |80 | | |Multitask network |15 |75 | | |robust MT-NN |15 |150 | | |random forest |15 |2200 | | |IRV |15 |150 | | |graph convolution |20 |50 | |toxcast |logistic regression |80 |2600 | | |Multitask network |80 |2300 | | |robust MT-NN |80 |4000 | | |graph convolution |80 |900 | |clintox |logistic regression |15 |10 | | |Multitask network |15 |20 | | |robust MT-NN |15 |30 | | |random forest |15 |200 | | |IRV |15 |10 | | |graph convolution |20 |130 | |delaney |MT-NN regression |10 |40 | | |graphconv regression|10 |40 | | |random forest |10 |30 | |sampl |MT-NN regression |10 |30 | | |graphconv regression|10 |40 | | |random forest |10 |20 | |nci |MT-NN regression |400 |1200 | | |graphconv regression|400 |2500 | |pdbbind(core) |MT-NN regression |0(featurized) |30 | |pdbbind(refined)|MT-NN regression |0(featurized) |40 | |pdbbind(full) |MT-NN regression |0(featurized) |60 | |chembl |MT-NN regression |200 |9000 | |gdb7 |MT-NN regression |10 |110 | |qm7 |MT-NN regression |10 |400 | |qm7b |MT-NN regression |10 |600 | |kaggle |MT-NN regression |2200 |3200 | Loading
deepchem/trans/transformers.py +34 −0 Original line number Diff line number Diff line Loading @@ -633,6 +633,25 @@ class IRVTransformer(): self.transform_y = transform_y def realize(self, similarity, y, w): """find samples with top ten similarity values in the reference dataset Parameters: ----------- similarity: np.ndarray similarity value between target dataset and reference dataset should have size of (n_samples_in_target, n_samples_in_reference) y: np.array labels for a single task w: np.array weights for a single task Return: ---------- features: list n_samples * np.array of size (2*K,) each array includes K similarity values and corresponding labels """ features = [] similarity_xs = similarity * np.sign(w) for similarity_x in similarity_xs: Loading @@ -646,6 +665,21 @@ class IRVTransformer(): return features def X_transform(self, X_target): """ Calculate similarity between target dataset(X_target) and reference dataset(X): #(1 in intersection)/#(1 in union) similarity = (X_target ∩ X)/(X_target U X) Parameters: ----------- X_target: np.ndarray fingerprints of target dataset should have same length with X in the second axis Returns: ---------- X_target: np.ndarray features of size(batch_size, 2*K*n_tasks) """ X_target2 = [] n_features = X_target.shape[1] similarity = np.matmul(X_target, np.transpose(self.X)) / ( Loading
