Commit 6835a33a authored by miaecle's avatar miaecle
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update

parent ca7335fd

delaney_dag_valid.py

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from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals



import deepchem

import numpy as np

import tensorflow as tf



seed = 123

np.random.seed(seed)



tasks, datasets, transformers = deepchem.molnet.load_delaney(featurizer='GraphConv', split='random', reload=False)

train_dataset, valid_dataset, test_dataset = datasets

metric = [deepchem.metrics.Metric(deepchem.metrics.rms_score, np.mean)]



max_atoms_train = max([mol.get_num_atoms() for mol in train_dataset.X])

max_atoms_valid = max([mol.get_num_atoms() for mol in valid_dataset.X])

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])



reshard_size = 512

transformer = deepchem.trans.DAGTransformer(max_atoms=max_atoms)

train_dataset.reshard(reshard_size)

train_dataset = transformer.transform(train_dataset)

valid_dataset.reshard(reshard_size)

valid_dataset = transformer.transform(valid_dataset)

test_dataset.reshard(reshard_size)

test_dataset = transformer.transform(test_dataset)



batch_size = 128

nb_epoch = 1000

learning_rate = 0.0005

n_graph_feat = 23



tf.set_random_seed(seed)

model = deepchem.models.DAGTensorGraph(

               1, 

               max_atoms=55,

               n_atom_feat=75,

               n_graph_feat=n_graph_feat,

               mode='regression',

               batch_size=batch_size,

               leanring_rate=learning_rate,

               use_queue=False)



model.fit(train_dataset, nb_epoch=nb_epoch)

train_scores = model.evaluate(train_dataset, metric, transformers)

valid_scores = model.evaluate(valid_dataset, metric, transformers)

test_scores = model.evaluate(test_dataset, metric, transformers)



""" Expected Results:

  train_scores: {'mean-rms_score': 0.029829638487211169}

    

  valid_scores: {'mean-rms_score': 0.75142478279661051}

    

  test_scores: {'mean-rms_score': 0.53192168238754678}



"""
 
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delaney_graphconv_valid.py

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from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals



import deepchem

import numpy as np

import tensorflow as tf



seed = 123

np.random.seed(seed)



tasks, datasets, transformers = deepchem.molnet.load_delaney(featurizer='GraphConv', split='random', reload=False)

train_dataset, valid_dataset, test_dataset = datasets

metric = [deepchem.metrics.Metric(deepchem.metrics.rms_score, np.mean)]

  

batch_size = 150

nb_epoch = 1000

learning_rate = 0.0008



tf.set_random_seed(seed)

model = deepchem.models.GraphConvTensorGraph(1, mode='regression',

                                             batch_size=batch_size,

                                             leanring_rate=learning_rate)



model.fit(train_dataset, nb_epoch=nb_epoch)

train_scores = model.evaluate(train_dataset, metric, transformers)

valid_scores = model.evaluate(valid_dataset, metric, transformers)

test_scores = model.evaluate(test_dataset, metric, transformers)



""" Expected Results:

  train_scores: {'mean-rms_score': 0.058672648022210311}

    

  valid_scores: {'mean-rms_score': 0.3635136142334261}

    

  test_scores: {'mean-rms_score': 0.35664025829369983}



"""
 
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devtools/jenkins/MolNet.sh

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@@ -9,16 +9,16 @@ rm examples/results.csv || true 
cd examples

python benchmark.py -d tox21 -m weave -m graphconv -m tf_robust -m tf -m irv -m xgb -m logreg --seed 123

export retval_tox21=$?

python benchmark.py -d hiv -m weave -m graphconv -m tf -m irv -m logreg --seed 123

python benchmark.py -d hiv -m graphconv -m tf -m irv -m logreg --seed 123

export retval_hiv=$?

python benchmark.py -d delaney -m weave_regression -m graphconreg -m krr -m tf_regression -m xgb_regression -m mpnn --seed 123

python benchmark.py -d delaney -m weave_regression -m graphconreg -m tf_regression -m xgb_regression -m mpnn --seed 123

export retval_delaney=$?

python benchmark.py -d qm7 -m dtnn -m graphconvreg -m krr_ft -m krr -m tf_regression_ft -m tf_regression --seed 123

python benchmark.py -d qm7 -m dtnn -m graphconvreg -m tf_regression_ft -m tf_regression --seed 123

export retval_qm7=$?



cd ..

nosetests -v devtools/jenkins/compare_results.py --with-xunit || true

export retval=$?

#cd ..

#nosetests -v devtools/jenkins/compare_results.py --with-xunit || true

#export retval=$?



source deactivate

conda remove --name $envname --all

devtools/jenkins/compare_results.py

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@@ -12,7 +12,7 @@ BENCHMARK_TO_DESIRED_KEY_MAP = { 
    "tf": "NN classification",

    "tf_robust": "robust NN",

    "tf_regression": "NN regression",

    "tf_regression_ft": "NN regression(CM)"

    "tf_regression_ft": "NN regression(CM)",

    "graphconv": "Graph convolution",

    "graphconvreg": "Graphconv regression",

    "irv": "IRV",

qm9_dtnn_test.py

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from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals



import os

import deepchem

import numpy as np

import tensorflow as tf

import tempfile



data_dir = deepchem.utils.get_data_dir()

dataset_file = os.path.join(data_dir, "gdb9.sdf")



qm9_tasks = ["u0_atom"]

featurizer = deepchem.feat.CoulombMatrix(29)



loader = deepchem.data.SDFLoader(

        tasks=qm9_tasks,

        smiles_field="smiles",

        mol_field="mol",

        featurizer=featurizer)



dataset = loader.featurize(dataset_file)

splitter = deepchem.splits.RandomSplitter()

train_dataset, valid_dataset, test_dataset = splitter.train_valid_test_split(

      dataset)



transformers = [

      deepchem.trans.NormalizationTransformer(

          transform_y=True, dataset=train_dataset)

]

for transformer in transformers:

  train_dataset = transformer.transform(train_dataset)

  valid_dataset = transformer.transform(valid_dataset)

  test_dataset = transformer.transform(test_dataset)



metric = [deepchem.metrics.Metric(deepchem.metrics.mean_absolute_error, np.mean)]

  

batch_size = 49

nb_epoch = 100

learning_rate = 0.0003

n_embedding = 42

n_distance = 173



seed = 123

np.random.seed(seed)

tf.set_random_seed(seed)

model_dir = tempfile.mkdtemp()

model = deepchem.models.DTNNTensorGraph(

    len(qm9_tasks),

    n_embedding=n_embedding,

    n_hidden=60,

    n_distance=n_distance,

    distance_min=-1.,

    distance_max=18.,

    output_activation=False,

    batch_size=batch_size,

    learning_rate=learning_rate,

    use_queue=False,

    mode="regression",

    model_dir=model_dir)

model.fit(train_dataset, nb_epoch=nb_epoch)

for rate in [learning_rate/5, learning_rate/20, learning_rate/100]:

  model = deepchem.models.DTNNTensorGraph(

      len(qm9_tasks),

      n_embedding=n_embedding,

      n_hidden=60,

      n_distance=n_distance,

      distance_min=-1.,

      distance_max=18.,

      output_activation=False,

      batch_size=batch_size,

      learning_rate=learning_rate/5,

      use_queue=False,

      mode="regression",

      model_dir=model_dir)

  model.restore()

  model.fit(train_dataset, nb_epoch=10)



train_scores = model.evaluate(train_dataset, metric, transformers)

valid_scores = model.evaluate(valid_dataset, metric, transformers)

test_scores = model.evaluate(test_dataset, metric, transformers)



model.fit(train_dataset, nb_epoch=10)

'''

computed_metrics: [0.95282979862675088]

computed_metrics: [1.1501283330568968]

computed_metrics: [1.2601717317672092]

'''
 
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