Merge pull request #353 from joegomes/gdb7-example

    raw_df = next(load_csv_files([input_file+".csv"], shard_size=None))

    # Structures are stored in .sdf file

    print("Reading structures from %s." % input_file)

    suppl = Chem.SDMolSupplier(str(input_file), removeHs=False)

    suppl = Chem.SDMolSupplier(str(input_file), False, False, False)

    df_rows = []

    for ind, mol in enumerate(suppl):

      if mol is not None:

"""

Script that trains Sklearn singletask models on GDB7 dataset.

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

import os

import deepchem as dc

import numpy as np

import shutil

from sklearn.kernel_ridge import KernelRidge

np.random.seed(123)

base_dir = "/tmp/gdb7_sklearn"

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

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

train_dir = os.path.join(base_dir, "train")

test_dir = os.path.join(base_dir, "test")

if os.path.exists(base_dir):

  shutil.rmtree(base_dir)

os.makedirs(base_dir)

max_num_atoms = 23

featurizers = dc.feat.CoulombMatrixEig(max_num_atoms)

input_file = "gdb7.sdf"

tasks = ["u0_atom"]

smiles_field = "smiles"

mol_field = "mol"

featurizer = dc.data.SDFLoader(tasks, smiles_field=smiles_field, mol_field=mol_field, featurizer=featurizers)

dataset = featurizer.featurize(input_file, data_dir)

random_splitter = dc.splits.RandomSplitter()

train_dataset, test_dataset = random_splitter.train_test_split(dataset, train_dir, test_dir)

transformers = [dc.trans.NormalizationTransformer(transform_X=True, dataset=train_dataset), dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset)]

#transformers = [dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset)]

for transformer in transformers:

    train_dataset = transformer.transform(train_dataset)

for transformer in transformers:

    test_dataset = transformer.transform(test_dataset)

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

def model_builder(model_dir):

  sklearn_model = KernelRidge(

      kernel="laplacian", alpha=0.05, gamma=0.1)

  return dc.models.SklearnModel(sklearn_model, model_dir)

model = dc.models.SingletaskToMultitask(tasks, model_builder, model_dir)

# Fit trained model

model.fit(train_dataset)

model.save()

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

train_scores = train_evaluator.compute_model_performance([regression_metric])

print("Train scores [kcal/mol]")

print(train_scores)

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

test_scores = test_evaluator.compute_model_performance([regression_metric])

print("Validation scores [kcal/mol]")

print(test_scores)

"""

Script that trains Tensorflow singletask models on GDB7 dataset.

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

import os

import deepchem as dc

import numpy as np

import shutil

from sklearn.kernel_ridge import KernelRidge

np.random.seed(123)

base_dir = "/tmp/gdb7_sklearn"

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

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

train_dir = os.path.join(base_dir, "train")

test_dir = os.path.join(base_dir, "test")

if os.path.exists(base_dir):

  shutil.rmtree(base_dir)

os.makedirs(base_dir)

max_num_atoms = 23

featurizers = dc.feat.CoulombMatrixEig(max_num_atoms)

input_file = "gdb7.sdf"

tasks = ["u0_atom"]

smiles_field = "smiles"

mol_field = "mol"

featurizer = dc.data.SDFLoader(tasks, smiles_field=smiles_field, mol_field=mol_field, featurizer=featurizers)

dataset = featurizer.featurize(input_file, data_dir)

random_splitter = dc.splits.RandomSplitter()

train_dataset, test_dataset = random_splitter.train_test_split(dataset, train_dir, test_dir)

transformers = [dc.trans.NormalizationTransformer(transform_X=True, dataset=train_dataset), dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset)]

for transformer in transformers:

    train_dataset = transformer.transform(train_dataset)

for transformer in transformers:

    test_dataset = transformer.transform(test_dataset)

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

model = dc.models.TensorflowMultiTaskRegressor(n_tasks=len(tasks), n_features=23, logdir=model_dir,

                                    learning_rate=.001, momentum=.8, batch_size=512,

                                    weight_init_stddevs=[1/np.sqrt(2000),1/np.sqrt(800),1/np.sqrt(800),1/np.sqrt(1000)],

                                    bias_init_consts=[0.,0.,0.,0.], layer_sizes=[2000,800,800,1000], 

                                    dropouts=[0.1,0.1,0.1,0.1])

# Fit trained model

model.fit(train_dataset)

model.save()

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

train_scores = train_evaluator.compute_model_performance([regression_metric])

print("Train scores [kcal/mol]")

print(train_scores)

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

test_scores = test_evaluator.compute_model_performance([regression_metric])

print("Validation scores [kcal/mol]")

print(test_scores)

echo "Pulling GDB7 dataset from deepchem"

wget http://deepchem.io.s3-website-us-west-1.amazonaws.com/datasets/gdb7.tar.gz

echo "Extracting gdb7 structures"

tar -zxvf gdb7.tar.gz