Merge remote-tracking branch 'remotes/origin/master' into HEAD

You can install deepchem in a new conda environment using the conda commands in scripts/install_deepchem_conda.sh

```bash

git clone https://github.com/deepchem/deepchem.git      # Clone deepchem source code from GitHub

bash scripts/install_deepchem_conda.sh deepchem

pip install tensorflow-gpu==1.0.1                      # If you want GPU support

git clone https://github.com/deepchem/deepchem.git      # Clone deepchem source code from GitHub

cd deepchem

python setup.py install                                 # Manual install

nosetests -v deepchem --nologcapture                    # Run tests

echo "Pulling featurized and split ACNN datasets from deepchem"

wget http://deepchem.io.s3-website-us-west-1.amazonaws.com/featurized_datasets/acnn_core.tar.gz

echo "Extracting ACNN datasets"

tar -zxvf acnn_core.tar.gz

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

__author__ = "Joseph Gomes"

__copyright__ = "Copyright 2017, Stanford University"

__license__ = "MIT"

import os

import sys

import deepchem as dc

import numpy as np

import tensorflow as tf

sys.path.append("../../models")

from atomicnet_ops import create_symmetry_parameters

from atomicnet import TensorflowFragmentRegressor

seed = 123

np.random.seed(seed)

tf.set_random_seed(seed)

base_dir = os.getcwd()

data_dir = os.path.join(base_dir, "datasets")

train_dir = os.path.join(data_dir, "random_train")

test_dir = os.path.join(data_dir, "random_test")

model_dir = os.path.join(base_dir, "random_model")

frag1_num_atoms = 140

frag2_num_atoms = 821

complex_num_atoms = 908

max_num_neighbors = 12

neighbor_cutoff = 12.0

train_dataset = dc.data.DiskDataset(train_dir)

test_dataset = dc.data.DiskDataset(test_dir)

pdbbind_tasks = ["-logKd/Ki"]

transformers = [

    dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset)

]

for transformer in transformers:

  train_dataset = transformer.transform(train_dataset)

  test_dataset = transformer.transform(test_dataset)

at = [1., 6, 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.]

radial = [[12.0], [0.0, 4.0, 8.0], [4.0]]

rp = create_symmetry_parameters(radial)

layer_sizes = [32, 32, 16]

weight_init_stddevs = [

    1 / np.sqrt(layer_sizes[0]), 1 / np.sqrt(layer_sizes[1]),

    1 / np.sqrt(layer_sizes[2])

]

dropouts = [0., 0., 0.]

penalty_type = "l2"

penalty = 0.

model = TensorflowFragmentRegressor(

    len(pdbbind_tasks),

    rp,

    at,

    frag1_num_atoms,

    frag2_num_atoms,

    complex_num_atoms,

    max_num_neighbors,

    logdir=model_dir,

    layer_sizes=layer_sizes,

    weight_init_stddevs=weight_init_stddevs,

    bias_init_consts=[0., 0., 0.],

    penalty=penalty,

    penalty_type=penalty_type,

    dropouts=dropouts,

    learning_rate=0.002,

    momentum=0.8,

    optimizer="adam",

    batch_size=24,

    conv_layers=1,

    boxsize=None,

    verbose=True,

    seed=seed)

model.fit(train_dataset, nb_epoch=100)

metric = [

    dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"),

    dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression")

]

train_evaluator = dc.utils.evaluate.Evaluator(model, train_dataset,

                                              transformers)

train_scores = train_evaluator.compute_model_performance(metric)

print("Train scores")

print(train_scores)

test_evaluator = dc.utils.evaluate.Evaluator(model, test_dataset, transformers)

test_scores = test_evaluator.compute_model_performance(metric)

print("Test scores")

print(test_scores)

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

__author__ = "Joseph Gomes"

__copyright__ = "Copyright 2017, Stanford University"

__license__ = "MIT"

import os

import sys

import deepchem as dc

import numpy as np

import tensorflow as tf

sys.path.append("../../models")

from atomicnet_ops import create_symmetry_parameters

from atomicnet import TensorflowFragmentRegressor

seed = 123

np.random.seed(seed)

tf.set_random_seed(seed)

base_dir = os.getcwd()

data_dir = os.path.join(base_dir, "datasets")

train_dir = os.path.join(data_dir, "scaffold_train")

test_dir = os.path.join(data_dir, "scaffold_test")

model_dir = os.path.join(base_dir, "scaffold_model")

frag1_num_atoms = 140

frag2_num_atoms = 821

complex_num_atoms = 908

max_num_neighbors = 12

neighbor_cutoff = 12.0

train_dataset = dc.data.DiskDataset(train_dir)

test_dataset = dc.data.DiskDataset(test_dir)

pdbbind_tasks = ["-logKd/Ki"]

transformers = [

    dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset)

]

for transformer in transformers:

  train_dataset = transformer.transform(train_dataset)

  test_dataset = transformer.transform(test_dataset)

at = [1., 6, 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.]

radial = [[12.0], [0.0, 4.0, 8.0], [4.0]]

rp = create_symmetry_parameters(radial)

layer_sizes = [32, 32, 16]

weight_init_stddevs = [

    1 / np.sqrt(layer_sizes[0]), 1 / np.sqrt(layer_sizes[1]),

    1 / np.sqrt(layer_sizes[2])

]

dropouts = [0., 0., 0.]

penalty_type = "l2"

penalty = 0.

model = TensorflowFragmentRegressor(

    len(pdbbind_tasks),

    rp,

    at,

    frag1_num_atoms,

    frag2_num_atoms,

    complex_num_atoms,

    max_num_neighbors,

    logdir=model_dir,

    layer_sizes=layer_sizes,

    weight_init_stddevs=weight_init_stddevs,

    bias_init_consts=[0., 0., 0.],

    penalty=penalty,

    penalty_type=penalty_type,

    dropouts=dropouts,

    learning_rate=0.002,

    momentum=0.8,

    optimizer="adam",

    batch_size=24,

    conv_layers=1,

    boxsize=None,

    verbose=True,

    seed=seed)

model.fit(train_dataset, nb_epoch=100)

metric = [

    dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"),

    dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression")

]

train_evaluator = dc.utils.evaluate.Evaluator(model, train_dataset,

                                              transformers)

train_scores = train_evaluator.compute_model_performance(metric)

print("Train scores")

print(train_scores)

test_evaluator = dc.utils.evaluate.Evaluator(model, test_dataset, transformers)

test_scores = test_evaluator.compute_model_performance(metric)

print("Test scores")

print(test_scores)

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

__author__ = "Joseph Gomes"

__copyright__ = "Copyright 2017, Stanford University"

__license__ = "MIT"

import os

import sys

import deepchem as dc

import numpy as np

import tensorflow as tf

sys.path.append("../../models")

from atomicnet_ops import create_symmetry_parameters

from atomicnet import TensorflowFragmentRegressor

seed = 123

np.random.seed(seed)

tf.set_random_seed(seed)

base_dir = os.getcwd()

data_dir = os.path.join(base_dir, "datasets")

train_dir = os.path.join(data_dir, "stratified_train")

test_dir = os.path.join(data_dir, "stratified_test")

model_dir = os.path.join(base_dir, "stratified_model")

frag1_num_atoms = 140

frag2_num_atoms = 821

complex_num_atoms = 908

max_num_neighbors = 12

neighbor_cutoff = 12.0

train_dataset = dc.data.DiskDataset(train_dir)

test_dataset = dc.data.DiskDataset(test_dir)

pdbbind_tasks = ["-logKd/Ki"]

transformers = [

    dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset)

]

for transformer in transformers:

  train_dataset = transformer.transform(train_dataset)

  test_dataset = transformer.transform(test_dataset)

at = [1., 6, 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.]

radial = [[12.0], [0.0, 4.0, 8.0], [4.0]]

rp = create_symmetry_parameters(radial)

layer_sizes = [32, 32, 16]

weight_init_stddevs = [

    1 / np.sqrt(layer_sizes[0]), 1 / np.sqrt(layer_sizes[1]),

    1 / np.sqrt(layer_sizes[2])

]

dropouts = [0., 0., 0.]

penalty_type = "l2"

penalty = 0.

model = TensorflowFragmentRegressor(

    len(pdbbind_tasks),

    rp,

    at,

    frag1_num_atoms,

    frag2_num_atoms,

    complex_num_atoms,

    max_num_neighbors,

    logdir=model_dir,

    layer_sizes=layer_sizes,

    weight_init_stddevs=weight_init_stddevs,

    bias_init_consts=[0., 0., 0.],

    penalty=penalty,

    penalty_type=penalty_type,

    dropouts=dropouts,

    learning_rate=0.002,

    momentum=0.8,

    optimizer="adam",

    batch_size=24,

    conv_layers=1,

    boxsize=None,

    verbose=True,

    seed=seed)

model.fit(train_dataset, nb_epoch=100)

metric = [

    dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"),

    dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression")

]

train_evaluator = dc.utils.evaluate.Evaluator(model, train_dataset,

                                              transformers)

train_scores = train_evaluator.compute_model_performance(metric)

print("Train scores")

print(train_scores)

test_evaluator = dc.utils.evaluate.Evaluator(model, test_dataset, transformers)

test_scores = test_evaluator.compute_model_performance(metric)

print("Test scores")

print(test_scores)