Commit 293073fb authored by miaecle's avatar miaecle
Browse files

wrap up

parent 0ee319d9

README.md

+21 −5
Original line number Diff line number Diff line
@@ -220,7 +220,7 @@ Index splitting 
|           |MT-NN classification|0.934              |0.830              |

|           |Robust MT-NN        |0.949              |0.827              |

|           |Graph convolution   |0.946              |0.860              |

|           |Weave               |0.907              |0.879              |

|           |Weave               |0.942              |0.917              |

|hiv        |Logistic regression |0.864              |0.739              |

|           |Random forest       |0.999              |0.720              |

|           |XGBoost             |0.917              |0.745              |
@@ -228,11 +228,13 @@ Index splitting 
|           |NN classification   |0.761              |0.652              |

|           |Robust NN           |0.780              |0.708              |

|           |Graph convolution   |0.876              |0.779              |

|           |Weave               |0.907              |0.753              |

|muv        |Logistic regression |0.963              |0.766              |

|           |XGBoost             |0.895              |0.714              |

|           |MT-NN classification|0.904              |0.764              |

|           |Robust MT-NN        |0.934              |0.781              |

|           |Graph convolution   |0.840              |0.823              |

|           |Weave               |0.762              |0.761              |

|pcba       |Logistic regression |0.809              |0.776              |

|           |XGBoost             |0.931              |0.847              |

|           |MT-NN classification|0.826              |0.802              |
@@ -245,6 +247,7 @@ Index splitting 
|           |MT-NN classification|0.775              |0.634              |

|           |Robust MT-NN        |0.803              |0.632              |

|           |Graph convolution   |0.708              |0.594              |

|           |Weave               |0.591              |0.580              |

|tox21      |Logistic regression |0.903              |0.705              |

|           |Random forest       |0.999              |0.733              |

|           |XGBoost             |0.891              |0.753              |
@@ -258,6 +261,7 @@ Index splitting 
|           |MT-NN classification|0.830              |0.678              |

|           |Robust MT-NN        |0.825              |0.680              |

|           |Graph convolution   |0.821              |0.720              |

|           |Weave               |0.766              |0.715              |



Random splitting
@@ -269,12 +273,14 @@ Random splitting 
|           |NN classification   |0.877              |0.790              |

|           |Robust NN           |0.887              |0.864              |

|           |Graph convolution   |0.906              |0.861              |

|           |Weave               |0.807              |0.780              |

|bbbp       |Logistic regression |0.980              |0.876              |

|           |Random forest       |0.999              |0.918              |

|           |IRV                 |0.904              |0.917              |

|           |NN classification   |0.882              |0.915              |

|           |Robust NN           |0.878              |0.878              |

|           |Graph convolution   |0.962              |0.897              |

|           |Weave               |0.929              |0.934              |

|clintox    |Logistic regression |0.972              |0.725              |

|           |Random forest       |0.997              |0.670              |

|           |XGBoost             |0.886              |0.731              |
@@ -282,7 +288,7 @@ Random splitting 
|           |MT-NN classification|0.951              |0.834              |

|           |Robust MT-NN        |0.959              |0.830              |

|           |Graph convolution   |0.975              |0.876              |

|           |Weave               |0.890              |0.738              |

|           |Weave               |0.945              |0.818              |

|hiv        |Logistic regression |0.860              |0.806              |

|           |Random forest       |0.999              |0.850              |

|           |XGBoost             |0.933              |0.841              |
@@ -290,11 +296,13 @@ Random splitting 
|           |NN classification   |0.742              |0.715              |

|           |Robust NN           |0.753              |0.727              |

|           |Graph convolution   |0.847              |0.803              |

|           |Weave               |0.902              |0.825              |

|muv        |Logistic regression |0.957              |0.719              |

|           |XGBoost             |0.874              |0.696              |

|           |MT-NN classification|0.902              |0.734              |

|           |Robust MT-NN        |0.933              |0.732              |

|           |Graph convolution   |0.860              |0.730              |

|           |Weave               |0.763              |0.763              |

|pcba       |Logistic regression |0.808        	     |0.776              |

|           |MT-NN classification|0.811        	     |0.778              |

|           |Robust MT-NN        |0.811              |0.771              |
@@ -306,6 +314,7 @@ Random splitting 
|           |MT-NN classification|0.777        	     |0.655              |

|           |Robust MT-NN        |0.804              |0.630              |

|           |Graph convolution   |0.705        	     |0.618              |

|           |Weave               |0.616              |0.645              |

|tox21      |Logistic regression |0.902              |0.715              |

|           |Random forest       |0.999              |0.764              |

|           |XGBoost             |0.874              |0.773              |
@@ -313,12 +322,13 @@ Random splitting 
|           |MT-NN classification|0.844              |0.795              |

|           |Robust MT-NN        |0.855              |0.773              |

|           |Graph convolution   |0.865              |0.827              |

|           |Weave               |0.796              |0.781              |

|           |Weave               |0.837              |0.830              |

|toxcast    |Logistic regression |0.725        	     |0.586              |

|           |XGBoost             |0.738              |0.633              |

|           |MT-NN classification|0.836        	     |0.684              |

|           |Robust MT-NN        |0.822              |0.681              |

|           |Graph convolution   |0.820        	     |0.717              |

|           |Weave               |0.757              |0.729              |



Scaffold splitting
@@ -330,12 +340,14 @@ Scaffold splitting 
|           |NN classification   |0.897              |0.743              |

|           |Robust NN           |0.910              |0.747              |

|           |Graph convolution   |0.920              |0.682              |

|           |Weave               |0.860              |0.629              |

|bbbp       |Logistic regression |0.980              |0.959              |

|           |Random forest       |0.999              |0.953              |

|           |IRV                 |0.914              |0.961              |

|           |NN classification   |0.899              |0.961              |

|           |Robust NN           |0.908              |0.956              |

|           |Graph convolution   |0.968              |0.950              |

|           |Weave               |0.925              |0.968              |

|clintox    |Logistic regression |0.965              |0.688              |

|           |Random forest       |0.993              |0.735              |

|           |XGBoost             |0.873              |0.850              |
@@ -343,7 +355,7 @@ Scaffold splitting 
|           |MT-NN classification|0.937              |0.828              |

|           |Robust MT-NN        |0.956              |0.821              |

|           |Graph convolution   |0.965              |0.900              |

|           |Weave               |0.888              |0.873              |

|           |Weave               |0.950              |0.947              |

|hiv        |Logistic regression |0.858              |0.798              |

|           |Random forest       |0.946              |0.562              |

|           |XGBoost             |0.927              |0.830              |
@@ -351,11 +363,13 @@ Scaffold splitting 
|           |NN classification   |0.775              |0.765              |

|           |Robust NN           |0.785              |0.748              |

|           |Graph convolution   |0.867              |0.769              |

|           |Weave               |0.875              |0.816              |

|muv        |Logistic regression |0.947              |0.767              |

|           |XGBoost             |0.875              |0.705              |

|           |MT-NN classification|0.899              |0.762              |

|           |Robust MT-NN        |0.944              |0.726              |

|           |Graph convolution   |0.872              |0.795              |

|           |Weave               |0.780              |0.773              |

|pcba       |Logistic regression |0.810              |0.742              |

|           |MT-NN classification|0.814              |0.760              |

|           |Robust MT-NN        |0.812              |0.756              |
@@ -367,6 +381,7 @@ Scaffold splitting 
|           |MT-NN classification|0.776              |0.557              |

|           |Robust MT-NN        |0.797              |0.560              |

|           |Graph convolution   |0.722              |0.583              |

|           |Weave               |0.600              |0.529              |

|tox21      |Logistic regression |0.900              |0.650              |

|           |Random forest       |0.999              |0.629              |

|           |XGBoost             |0.881              |0.703              |
@@ -374,12 +389,13 @@ Scaffold splitting 
|           |MT-NN classification|0.863              |0.703              |

|           |Robust MT-NN        |0.861              |0.710              |

|           |Graph convolution   |0.885              |0.732              |

|           |Weave               |0.812              |0.727              |

|           |Weave               |0.866              |0.773              |

|toxcast    |Logistic regression |0.716              |0.492              |

|           |XGBoost             |0.741              |0.587              |

|           |MT-NN classification|0.828              |0.617              |

|           |Robust MT-NN        |0.830              |0.614              |

|           |Graph convolution   |0.832              |0.638              |

|           |Weave               |0.766              |0.637              |





* Regression

deepchem/models/tf_new_models/graph_topology.py

+5 −12
Original line number Diff line number Diff line
@@ -525,11 +525,8 @@ class WeaveGraphTopology_v2(GraphTopology): 
        shape=(None, self.n_pair_feat),

        name=self.name + '_pair_features')

    self.pair_split_placeholder = tf.placeholder(

        dtype='int32', shape=(self.max_atoms,), 

        dtype='int32', shape=(None,), 

        name=self.name + '_pair_split')

    self.pair_membership_placeholder = tf.placeholder(

        dtype='bool', shape=(self.max_atoms,), 

        name=self.name + '_pair_membership')

    self.atom_split_placeholder = tf.placeholder(

        dtype='int32', shape=(self.batch_size,), 

        name=self.name + '_atom_split')
@@ -538,8 +535,7 @@ class WeaveGraphTopology_v2(GraphTopology): 
        name=self.name + '_atom_to_pair')

    

    # Define the list of tensors to be used as topology

    self.topology = [self.pair_split_placeholder, self.pair_membership_placeholder,

                     self.atom_split_placeholder, self.atom_to_pair_placeholder]

    self.topology = [self.pair_split_placeholder, self.atom_split_placeholder, self.atom_to_pair_placeholder]

    self.inputs = [self.atom_features_placeholder]

    self.inputs += self.topology
@@ -577,9 +573,10 @@ class WeaveGraphTopology_v2(GraphTopology): 
      # index of pair features

      C0, C1 = np.meshgrid(np.arange(n_atoms), np.arange(n_atoms))

      atom_to_pair.append(np.transpose(np.array([C1.flatten()+start, C0.flatten()+start])))

      start = start + n_atoms

      # number of pairs for each atom

      pair_split.extend([n_atoms]*n_atoms)

      pair_split.extend(C1.flatten()+start)

      start = start + n_atoms

    

      # atom features

      atom_feat.append(mol.get_atom_features())

      # pair features
@@ -590,15 +587,11 @@ class WeaveGraphTopology_v2(GraphTopology): 
    pair_feat = np.concatenate(pair_feat, axis=0)

    atom_to_pair = np.concatenate(atom_to_pair, axis=0)

    atom_split = np.array(atom_split)

    n_pair = len(pair_split)

    pair_split = np.pad(pair_split, ((0, max_atoms-n_pair)), 'constant')

    pair_membership = np.array([True]*n_pair + [False]*(max_atoms-n_pair))

    # Generate dicts

    dict_DTNN = {

        self.atom_features_placeholder: atom_feat,

        self.pair_features_placeholder: pair_feat,

        self.pair_split_placeholder: pair_split,

        self.pair_membership_placeholder: pair_membership,

        self.atom_split_placeholder: atom_split,

        self.atom_to_pair_placeholder: atom_to_pair

    }

deepchem/molnet/preset_hyper_parameters.py

+1 −1
Original line number Diff line number Diff line
@@ -68,7 +68,7 @@ hps['dag'] = { 
hps['weave'] = {

    'batch_size': 64,

    'nb_epoch': 40,

    'learning_rate': 0.0001,

    'learning_rate': 0.001,

    'n_graph_feat': 128,

    'n_pair_feat': 14,

    'seed': 123

deepchem/molnet/run_benchmark_models.py

+13 −13
Original line number Diff line number Diff line
@@ -271,14 +271,14 @@ def benchmark_classification(train_dataset, 
    max_atoms_test = max([mol.get_num_atoms() for mol in test_dataset.X])

    max_atoms = max([max_atoms_train, max_atoms_valid, max_atoms_test])

    

    graph_model = deepchem.nn.SequentialWeaveGraph(max_atoms=max_atoms,

        n_atom_feat=n_features, n_pair_feat=n_pair_feat)

    graph_model.add(deepchem.nn.WeaveLayer(max_atoms, 75, 14))

    graph_model.add(deepchem.nn.WeaveLayer(max_atoms, 50, 50, update_pair=False))

    graph_model.add(deepchem.nn.WeaveConcat(batch_size, n_output=n_graph_feat))

    graph_model = deepchem.nn.SequentialWeaveGraph_v2(batch_size,

        max_atoms=max_atoms, n_atom_feat=n_features, n_pair_feat=n_pair_feat)

    graph_model.add(deepchem.nn.WeaveLayer_v2(max_atoms, 75, 14))

    graph_model.add(deepchem.nn.WeaveLayer_v2(max_atoms, 50, 50, update_pair=False))

    graph_model.add(deepchem.nn.Dense(n_graph_feat, 50, activation='tanh'))

    graph_model.add(deepchem.nn.BatchNormalization(epsilon=1e-5, mode=1))

    graph_model.add(

        deepchem.nn.WeaveGather(

        deepchem.nn.WeaveGather_v2(

            batch_size, n_input=n_graph_feat, gaussian_expand=True))



    model = deepchem.models.MultitaskGraphClassifier(
@@ -595,14 +595,14 @@ def benchmark_regression(train_dataset, 
    max_atoms_test = max([mol.get_num_atoms() for mol in test_dataset.X])

    max_atoms = max([max_atoms_train, max_atoms_valid, max_atoms_test])

    

    graph_model = deepchem.nn.SequentialWeaveGraph(max_atoms=max_atoms,

        n_atom_feat=n_features, n_pair_feat=n_pair_feat)

    graph_model.add(deepchem.nn.WeaveLayer(max_atoms, 75, 14))

    graph_model.add(deepchem.nn.WeaveLayer(max_atoms, 50, 50, update_pair=False))

    graph_model.add(deepchem.nn.WeaveConcat(batch_size, n_output=n_graph_feat))

    graph_model = deepchem.nn.SequentialWeaveGraph_v2(batch_size,

        max_atoms=max_atoms, n_atom_feat=n_features, n_pair_feat=n_pair_feat)

    graph_model.add(deepchem.nn.WeaveLayer_v2(max_atoms, 75, 14))

    graph_model.add(deepchem.nn.WeaveLayer_v2(max_atoms, 50, 50, update_pair=False))

    graph_model.add(deepchem.nn.Dense(n_graph_feat, 50, activation='tanh'))

    graph_model.add(deepchem.nn.BatchNormalization(epsilon=1e-5, mode=1))

    graph_model.add(

        deepchem.nn.WeaveGather(

        deepchem.nn.WeaveGather_v2(

            batch_size, n_input=n_graph_feat, gaussian_expand=True))



    model = deepchem.models.MultitaskGraphRegressor(

deepchem/nn/weave_layers.py

+3 −6
Original line number Diff line number Diff line
@@ -202,17 +202,14 @@ class WeaveLayer_v2(WeaveLayer): 
    pair_features = x[1]



    pair_split = x[2]

    pair_membership = x[3]

    atom_split = x[4]

    atom_to_pair = x[5]

    atom_split = x[3]

    atom_to_pair = x[4]



    AA = tf.matmul(atom_features, self.W_AA) + self.b_AA

    AA = self.activation(AA)

    PA = tf.matmul(pair_features, self.W_PA) + self.b_PA

    PA = self.activation(PA)

    PAs = tf.split(PA, pair_split, axis=0)

    PA = [tf.reduce_sum(molecule, 0) for molecule in PAs]

    PA = tf.boolean_mask(PA, pair_membership)

    PA = tf.segment_sum(PA, pair_split)

    

    A = tf.matmul(tf.concat([AA, PA], 1), self.W_A) + self.b_A

    A = self.activation(A)

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