Added ChEMBL dataset with examples

"""

KAGGLE dataset loader.

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

import os

import time

import numpy as np

import deepchem as dc

import sys

sys.path.append(".")

from chembl_tasks import chembl_tasks

def remove_missing_entries(dataset):

    """Remove missing entries.

    Some of the datasets have missing entries that sneak in as zero'd out

    feature vectors. Get rid of them.

    """

    for i, (X, y, w, ids) in enumerate(dataset.itershards()):

        available_rows = X.any(axis=1)

        print("Shard %d has %d missing entries."

              % (i, np.count_nonzero(~available_rows)))

        X = X[available_rows]

        y = y[available_rows]

        w = w[available_rows]

        ids = ids[available_rows]

        dataset.set_shard(i, X, y, w, ids)

# Set shard size low to avoid memory problems.

def load_chembl(shard_size=2000, featurizer="ECFP", split='random'):

    """Load KAGGLE datasets. Does not do train/test split"""

    ############################################################## TIMING

    time1 = time.time()

    ############################################################## TIMING

    # Set some global variables up top

    current_dir = os.path.dirname(os.path.realpath(__file__))

    # Load dataset

    print("About to load ChEMBL dataset.")

    dataset_path = os.path.join(

        current_dir, "../../datasets/chembl.csv")

    # Featurize KAGGLE dataset

    print("About to featurize ChEMBL dataset.")

    if featurizer == 'ECFP':

        featurizer = dc.feat.CircularFingerprint(size=1024)

    elif featurizer == 'GraphConv':

        featurizer = dc.feat.ConvMolFeaturizer()

    loader = dc.data.CSVLoader(

        tasks=chembl_tasks, smiles_field="smiles", featurizer=featurizer)

    dataset = loader.featurize(dataset_path, shard_size=shard_size)

    # Initialize transformers

    print("About to transform data")

    transformers = [

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

    for transformer in transformers:

        dataset = transformer.transform(dataset)

    splitters = {'index': dc.splits.IndexSplitter(),

                 'random': dc.splits.RandomSplitter(),

                 'scaffold': dc.splits.ScaffoldSplitter()}

    splitter = splitters[split]

    print("Performing new split.")

    train, valid, test = splitter.train_valid_test_split(dataset)

    return chembl_tasks, (train, valid, test), transformers

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"""

Script that trains graph-conv models on Tox21 dataset.

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

import numpy as np

import tensorflow as tf

import deepchem as dc

from keras import backend as K

from chembl_datasets import load_chembl

# Only for debug!

np.random.seed(123)

g = tf.Graph()

sess = tf.Session(graph=g)

K.set_session(sess)

with g.as_default():

  tf.set_random_seed(123)

  chembl_tasks, datasets, transformers = load_chembl(

      featurizer='GraphConv', split='index')

  train_dataset, valid_dataset, test_dataset = datasets

  # Fit models

  metric = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean)

  # Do setup required for tf/keras models

  # Number of features on conv-mols

  n_feat = 71

  # Batch size of models

  batch_size = 128

  graph_model = dc.nn.SequentialGraph(n_feat)

  graph_model.add(dc.nn.GraphConv(128, activation='relu'))

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

  graph_model.add(dc.nn.GraphPool())

  graph_model.add(dc.nn.GraphConv(128, activation='relu'))

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

  graph_model.add(dc.nn.GraphPool())

  # Gather Projection

  graph_model.add(dc.nn.Dense(256, activation='relu'))

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

  graph_model.add(dc.nn.GraphGather(batch_size, activation="tanh"))

  # Dense post-processing layer

  with tf.Session() as sess:

    model = dc.models.MultitaskGraphRegressor(

      sess, graph_model, len(chembl_tasks), batch_size=batch_size,

      learning_rate=1e-3, learning_rate_decay_time=1000,

      optimizer_type="adam", beta1=.9, beta2=.999)

    # Fit trained model

    model.fit(train_dataset, nb_epoch=10)

    print("Evaluating model")

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

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

    # Only use for final evaluation

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

    print("Train scores")

    print(train_scores)

    print("Validation scores")

    print(valid_scores)

    print("Test scores")

    print(test_scores)

chembl_tasks = ['CHEMBL1075194', 'CHEMBL1075263', 'CHEMBL1287626', 'CHEMBL1649052', 'CHEMBL1681617', 'CHEMBL1764942',

                'CHEMBL1798', 'CHEMBL1800', 'CHEMBL1804', 'CHEMBL1821', 'CHEMBL1836', 'CHEMBL1878', 'CHEMBL1887',

                'CHEMBL1901', 'CHEMBL1914263', 'CHEMBL1941', 'CHEMBL1942', 'CHEMBL1945', 'CHEMBL1963', 'CHEMBL1983',

                'CHEMBL2016428', 'CHEMBL2024', 'CHEMBL2107', 'CHEMBL211', 'CHEMBL2114', 'CHEMBL2115', 'CHEMBL2121',

                'CHEMBL2176850', 'CHEMBL2181', 'CHEMBL223', 'CHEMBL2286', 'CHEMBL2384898', 'CHEMBL2434', 'CHEMBL2488',

                'CHEMBL250', 'CHEMBL2514', 'CHEMBL253', 'CHEMBL255', 'CHEMBL2564', 'CHEMBL2605', 'CHEMBL263',

                'CHEMBL2647', 'CHEMBL2718', 'CHEMBL276', 'CHEMBL2798', 'CHEMBL287', 'CHEMBL2955', 'CHEMBL2967',

                'CHEMBL2978', 'CHEMBL302', 'CHEMBL317', 'CHEMBL3197', 'CHEMBL3228', 'CHEMBL327', 'CHEMBL3278',

                'CHEMBL3297639', 'CHEMBL3322', 'CHEMBL3359', 'CHEMBL3360', 'CHEMBL3374', 'CHEMBL339', 'CHEMBL3459',

                'CHEMBL3562166', 'CHEMBL3602', 'CHEMBL3611961', 'CHEMBL3636', 'CHEMBL3772', 'CHEMBL3798', 'CHEMBL3875',

                'CHEMBL3942', 'CHEMBL4123', 'CHEMBL4124', 'CHEMBL4333', 'CHEMBL4383', 'CHEMBL4552', 'CHEMBL4649',

                'CHEMBL4731', 'CHEMBL4761', 'CHEMBL4762', 'CHEMBL4843', 'CHEMBL4845', 'CHEMBL5136', 'CHEMBL5226',

                'CHEMBL5411', 'CHEMBL5412', 'CHEMBL5414', 'CHEMBL5739', 'CHEMBL5781', 'CHEMBL5844', 'CHEMBL5862',

                'CHEMBL5936', 'CHEMBL5968']

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