Commit 0b78522e authored by miaecle's avatar miaecle
Browse files

debug

parent 3f3e8b7a

deepchem/models/tensorgraph/layers.py

+14 −0
Original line number Diff line number Diff line
@@ -573,6 +573,20 @@ class Concat(Layer): 
    return out_tensor





class Stack(Layer):



  def __init__(self, axis=1, **kwargs):

    self.axis = axis

    super(Stack, self).__init__(**kwargs)



  def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):

    inputs = self._get_input_tensors(in_layers)

    out_tensor = tf.stack(inputs, axis=self.axis)

    if set_tensors:

      self.out_tensor = out_tensor

    return out_tensor

    

    

class Constant(Layer):

  """Output a constant value."""

deepchem/models/tensorgraph/models/graph_models.py

+12 −5
Original line number Diff line number Diff line
@@ -7,7 +7,7 @@ from deepchem.metrics import to_one_hot, from_one_hot 
from deepchem.models.tensorgraph.graph_layers import WeaveLayer, WeaveGather, \

    Combine_AP, Separate_AP, DTNNEmbedding, DTNNStep, DTNNGather, DAGLayer, DAGGather, DTNNExtract

from deepchem.models.tensorgraph.layers import Dense, Concat, SoftMax, SoftMaxCrossEntropy, GraphConv, BatchNorm, \

    GraphPool, GraphGather, WeightedError, BatchNormalization

    GraphPool, GraphGather, WeightedError, BatchNormalization, Stack

from deepchem.models.tensorgraph.layers import L2Loss, Label, Weights, Feature

from deepchem.models.tensorgraph.tensor_graph import TensorGraph

from deepchem.trans import undo_transforms
@@ -103,7 +103,10 @@ class WeaveTensorGraph(TensorGraph): 
        self.labels_fd.append(label)

        cost = L2Loss(in_layers=[label, regression])

        costs.append(cost)

    if self.mode == "classification":

      all_cost = Concat(in_layers=costs, axis=1)

    elif self.mode == "regression":

      all_cost = Stack(in_layers=costs, axis=1)

    self.weights = Weights(shape=(None, self.n_tasks))

    loss = WeightedError(in_layers=[all_cost, self.weights])

    self.set_loss(loss)
@@ -256,7 +259,7 @@ class DTNNTensorGraph(TensorGraph): 
      cost = L2Loss(in_layers=[label, regression])

      costs.append(cost)



    all_cost = Concat(in_layers=costs, axis=1)

    all_cost = Stack(in_layers=costs, axis=1)

    self.weights = Weights(shape=(None, self.n_tasks))

    loss = WeightedError(in_layers=[all_cost, self.weights])

    self.set_loss(loss)
@@ -401,8 +404,10 @@ class DAGTensorGraph(TensorGraph): 
        self.labels_fd.append(label)

        cost = L2Loss(in_layers=[label, regression])

        costs.append(cost)



    if self.mode == "classification":

      all_cost = Concat(in_layers=costs, axis=1)

    elif self.mode == "regression":

      all_cost = Stack(in_layers=costs, axis=1)

    self.weights = Weights(shape=(None, self.n_tasks))

    loss = WeightedError(in_layers=[all_cost, self.weights])

    self.set_loss(loss)
@@ -537,8 +542,10 @@ class GraphConvTensorGraph(TensorGraph): 
        self.my_labels.append(label)

        cost = L2Loss(in_layers=[label, regression])

        costs.append(cost)



    if self.mode == "classification":

      entropy = Concat(in_layers=costs, axis=-1)

    elif self.mode == "regression":

      entropy = Stack(in_layers=costs, axis=1)

    self.my_task_weights = Weights(shape=(None, self.n_tasks))

    loss = WeightedError(in_layers=[entropy, self.my_task_weights])

    self.set_loss(loss)
@@ -595,7 +602,7 @@ class GraphConvTensorGraph(TensorGraph): 
    if not self.built:

      self.build()

    return self.evaluate_generator(

        self.default_generator(dataset),

        self.default_generator(dataset, predict=True),

        metrics,

        labels=self.my_labels,

        weights=[self.my_task_weights])

deepchem/models/tensorgraph/models/symmetry_function_regression.py

+3 −3
Original line number Diff line number Diff line
@@ -8,7 +8,7 @@ Created on Thu Jul 6 20:31:47 2017 
import numpy as np

import tensorflow as tf



from deepchem.models.tensorgraph.layers import Dense, Concat, WeightedError

from deepchem.models.tensorgraph.layers import Dense, Concat, WeightedError, Stack

from deepchem.models.tensorgraph.layers import L2Loss, Label, Weights, Feature

from deepchem.models.tensorgraph.tensor_graph import TensorGraph

from deepchem.models.tensorgraph.graph_layers import DTNNEmbedding
@@ -69,7 +69,7 @@ class BPSymmetryFunctionRegression(TensorGraph): 
      cost = L2Loss(in_layers=[label, output])

      costs.append(cost)



    all_cost = Concat(in_layers=costs, axis=0)

    all_cost = Stack(in_layers=costs, axis=1)

    self.weights = Weights(shape=(None, self.n_tasks))

    loss = WeightedError(in_layers=[all_cost, self.weights])

    self.set_loss(loss)
@@ -158,7 +158,7 @@ class ANIRegression(TensorGraph): 
      cost = L2Loss(in_layers=[label, output])

      costs.append(cost)



    all_cost = Concat(in_layers=costs, axis=0)

    all_cost = Stack(in_layers=costs, axis=1)

    self.weights = Weights(shape=(None, self.n_tasks))

    loss = WeightedError(in_layers=[all_cost, self.weights])

    self.set_loss(loss)

deepchem/models/tensorgraph/tensor_graph.py

+2 −0
Original line number Diff line number Diff line
@@ -276,6 +276,8 @@ class TensorGraph(Model): 
          result = np.array(sess.run(out_tensors, feed_dict=feed_dict))

          if len(result.shape) == 3:

            result = np.transpose(result, axes=[1, 0, 2])

          if self.mode == 'regression':

            result = np.squeeze(result, 2)

          result = undo_transforms(result, transformers)

          results.append(result)

        return np.concatenate(results, axis=0)

deepchem/molnet/check_availability.py

+25 −2
Original line number Diff line number Diff line
@@ -3,6 +3,7 @@ CheckFeaturizer = { 
    ('bace_c', 'tf'): ['ECFP', 1024],

    ('bace_c', 'tf_robust'): ['ECFP', 1024],

    ('bace_c', 'rf'): ['ECFP', 1024],

    ('bace_c', 'kernelsvm'): ['ECFP', 1024],

    ('bace_c', 'irv'): ['ECFP', 1024],

    ('bace_c', 'xgb'): ['ECFP', 1024],

    ('bace_c', 'graphconv'): ['GraphConv', 75],
@@ -12,6 +13,7 @@ CheckFeaturizer = { 
    ('bbbp', 'tf'): ['ECFP', 1024],

    ('bbbp', 'tf_robust'): ['ECFP', 1024],

    ('bbbp', 'rf'): ['ECFP', 1024],

    ('bbbp', 'kernelsvm'): ['ECFP', 1024],

    ('bbbp', 'irv'): ['ECFP', 1024],

    ('bbbp', 'xgb'): ['ECFP', 1024],

    ('bbbp', 'graphconv'): ['GraphConv', 75],
@@ -21,6 +23,7 @@ CheckFeaturizer = { 
    ('clintox', 'tf'): ['ECFP', 1024],

    ('clintox', 'tf_robust'): ['ECFP', 1024],

    ('clintox', 'rf'): ['ECFP', 1024],

    ('clintox', 'kernelsvm'): ['ECFP', 1024],

    ('clintox', 'irv'): ['ECFP', 1024],

    ('clintox', 'xgb'): ['ECFP', 1024],

    ('clintox', 'graphconv'): ['GraphConv', 75],
@@ -30,6 +33,7 @@ CheckFeaturizer = { 
    ('hiv', 'tf'): ['ECFP', 1024],

    ('hiv', 'tf_robust'): ['ECFP', 1024],

    ('hiv', 'rf'): ['ECFP', 1024],

    ('hiv', 'kernelsvm'): ['ECFP', 1024],

    ('hiv', 'irv'): ['ECFP', 1024],

    ('hiv', 'xgb'): ['ECFP', 1024],

    ('hiv', 'graphconv'): ['GraphConv', 75],
@@ -39,6 +43,7 @@ CheckFeaturizer = { 
    ('muv', 'tf'): ['ECFP', 1024],

    ('muv', 'tf_robust'): ['ECFP', 1024],

    ('muv', 'rf'): ['ECFP', 1024],

    ('muv', 'kernelsvm'): ['ECFP', 1024],

    ('muv', 'irv'): ['ECFP', 1024],

    ('muv', 'xgb'): ['ECFP', 1024],

    ('muv', 'graphconv'): ['GraphConv', 75],
@@ -49,7 +54,6 @@ CheckFeaturizer = { 
    ('pcba', 'logreg'): ['ECFP', 1024],

    ('pcba', 'tf'): ['ECFP', 1024],

    ('pcba', 'tf_robust'): ['ECFP', 1024],

    ('pcba', 'rf'): ['ECFP', 1024],

    ('pcba', 'irv'): ['ECFP', 1024],

    ('pcba', 'xgb'): ['ECFP', 1024],

    ('pcba', 'graphconv'): ['GraphConv', 75],
@@ -58,6 +62,7 @@ CheckFeaturizer = { 
    ('sider', 'tf'): ['ECFP', 1024],

    ('sider', 'tf_robust'): ['ECFP', 1024],

    ('sider', 'rf'): ['ECFP', 1024],

    ('sider', 'kernelsvm'): ['ECFP', 1024],

    ('sider', 'irv'): ['ECFP', 1024],

    ('sider', 'xgb'): ['ECFP', 1024],

    ('sider', 'graphconv'): ['GraphConv', 75],
@@ -70,6 +75,7 @@ CheckFeaturizer = { 
    ('tox21', 'tf'): ['ECFP', 1024],

    ('tox21', 'tf_robust'): ['ECFP', 1024],

    ('tox21', 'rf'): ['ECFP', 1024],

    ('tox21', 'kernelsvm'): ['ECFP', 1024],

    ('tox21', 'irv'): ['ECFP', 1024],

    ('tox21', 'xgb'): ['ECFP', 1024],

    ('tox21', 'graphconv'): ['GraphConv', 75],
@@ -82,83 +88,100 @@ CheckFeaturizer = { 
    ('toxcast', 'tf'): ['ECFP', 1024],

    ('toxcast', 'tf_robust'): ['ECFP', 1024],

    ('toxcast', 'rf'): ['ECFP', 1024],

    ('toxcast', 'kernelsvm'): ['ECFP', 1024],

    ('toxcast', 'irv'): ['ECFP', 1024],

    ('toxcast', 'xgb'): ['ECFP', 1024],

    ('toxcast', 'graphconv'): ['GraphConv', 75],

    ('toxcast', 'weave'): ['Weave', 75],

    ('bace_r', 'tf_regression'): ['ECFP', 1024],

    ('bace_r', 'rf_regression'): ['ECFP', 1024],

    ('bace_r', 'krr'): ['ECFP', 1024],

    ('bace_r', 'xgb_regression'): ['ECFP', 1024],

    ('bace_r', 'graphconvreg'): ['GraphConv', 75],

    ('bace_r', 'dag_regression'): ['GraphConv', 75],

    ('bace_r', 'weave_regression'): ['Weave', 75],

    ('chembl', 'tf_regression'): ['ECFP', 1024],

    ('chembl', 'rf_regression'): ['ECFP', 1024],

    ('chembl', 'krr'): ['ECFP', 1024],

    ('chembl', 'xgb_regression'): ['ECFP', 1024],

    ('chembl', 'graphconvreg'): ['GraphConv', 75],

    ('chembl', 'weave_regression'): ['Weave', 75],

    ('clearance', 'tf_regression'): ['ECFP', 1024],

    ('clearance', 'rf_regression'): ['ECFP', 1024],

    ('clearance', 'krr'): ['ECFP', 1024],

    ('clearance', 'xgb_regression'): ['ECFP', 1024],

    ('clearance', 'graphconvreg'): ['GraphConv', 75],

    ('clearance', 'dag_regression'): ['GraphConv', 75],

    ('clearance', 'weave_regression'): ['Weave', 75],

    ('delaney', 'tf_regression'): ['ECFP', 1024],

    ('delaney', 'rf_regression'): ['ECFP', 1024],

    ('delaney', 'krr'): ['ECFP', 1024],

    ('delaney', 'xgb_regression'): ['ECFP', 1024],

    ('delaney', 'graphconvreg'): ['GraphConv', 75],

    ('delaney', 'dag_regression'): ['GraphConv', 75],

    ('delaney', 'weave_regression'): ['Weave', 75],

    ('hopv', 'tf_regression'): ['ECFP', 1024],

    ('hopv', 'rf_regression'): ['ECFP', 1024],

    ('hopv', 'krr'): ['ECFP', 1024],

    ('hopv', 'xgb_regression'): ['ECFP', 1024],

    ('hopv', 'graphconvreg'): ['GraphConv', 75],

    ('hopv', 'dag_regression'): ['GraphConv', 75],

    ('hopv', 'weave_regression'): ['Weave', 75],

    ('lipo', 'tf_regression'): ['ECFP', 1024],

    ('lipo', 'rf_regression'): ['ECFP', 1024],

    ('lipo', 'krr'): ['ECFP', 1024],

    ('lipo', 'xgb_regression'): ['ECFP', 1024],

    ('lipo', 'graphconvreg'): ['GraphConv', 75],

    ('lipo', 'dag_regression'): ['GraphConv', 75],

    ('lipo', 'weave_regression'): ['Weave', 75],

    ('nci', 'tf_regression'): ['ECFP', 1024],

    ('nci', 'rf_regression'): ['ECFP', 1024],

    ('nci', 'krr'): ['ECFP', 1024],

    ('nci', 'xgb_regression'): ['ECFP', 1024],

    ('nci', 'graphconvreg'): ['GraphConv', 75],

    ('nci', 'weave_regression'): ['Weave', 75],

    ('ppb', 'tf_regression'): ['ECFP', 1024],

    ('ppb', 'rf_regression'): ['ECFP', 1024],

    ('ppb', 'krr'): ['ECFP', 1024],

    ('ppb', 'xgb_regression'): ['ECFP', 1024],

    ('ppb', 'graphconvreg'): ['GraphConv', 75],

    ('ppb', 'dag_regression'): ['GraphConv', 75],

    ('ppb', 'weave_regression'): ['Weave', 75],

    ('sampl', 'tf_regression'): ['ECFP', 1024],

    ('sampl', 'rf_regression'): ['ECFP', 1024],

    ('sampl', 'krr'): ['ECFP', 1024],

    ('sampl', 'xgb_regression'): ['ECFP', 1024],

    ('sampl', 'graphconvreg'): ['GraphConv', 75],

    ('sampl', 'dag_regression'): ['GraphConv', 75],

    ('sampl', 'weave_regression'): ['Weave', 75],

    ('kaggle', 'tf_regression'): [None, 14293],

    ('kaggle', 'rf_regression'): [None, 14293],

    ('kaggle', 'krr'): [None, 14293],

    ('pdbbind', 'tf_regression'): ['grid', 2052],

    ('pdbbind', 'rf_regression'): ['grid', 2052],

    ('qm7', 'tf_regression'): ['ECFP', 1024],

    ('qm7', 'rf_regression'): ['ECFP', 1024],

    ('qm7', 'krr'): ['ECFP', 1024],

    ('qm7', 'graphconvreg'): ['GraphConv', 75],

    ('qm7', 'tf_regression_ft'): ['CoulombMatrix', [23, 23]],

    ('qm7', 'dtnn'): ['CoulombMatrix', [23, 23]],

    ('qm7', 'ani'): ['BPSymmetryFunction', [23, 4]],

    ('qm7b', 'tf_regression_ft'): ['CoulombMatrix', [23, 23]],

    ('qm7b', 'dtnn'): ['CoulombMatrix', [23, 23]],

    ('qm8', 'tf_regression'): ['ECFP', 1024],

    ('qm8', 'rf_regression'): ['ECFP', 1024],

    ('qm8', 'krr'): ['ECFP', 1024],

    ('qm8', 'graphconvreg'): ['GraphConv', 75],

    ('qm8', 'tf_regression_ft'): ['CoulombMatrix', [26, 26]],

    ('qm8', 'dtnn'): ['CoulombMatrix', [26, 26]],

    ('qm8', 'ani'): ['BPSymmetryFunction', [26, 4]],

    ('qm9', 'tf_regression'): ['ECFP', 1024],

    ('qm9', 'rf_regression'): ['ECFP', 1024],

    ('qm9', 'krr'): ['ECFP', 1024],

    ('qm9', 'graphconvreg'): ['GraphConv', 75],

    ('qm9', 'tf_regression_ft'): ['CoulombMatrix', [29, 29]],

    ('qm9', 'dtnn'): ['CoulombMatrix', [29, 29]]

    ('qm9', 'dtnn'): ['CoulombMatrix', [29, 29]],

    ('qm9', 'ani'): ['BPSymmetryFunction', [29, 4]]

}



CheckSplit = {

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