scripts for training multitask and graphconv models on clintox dataset

"""

Script that trains graph-conv models on clintox dataset.

@author Caleb Geniesse

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

import numpy as np

import tensorflow as tf

from keras import backend as K

import deepchem as dc

from clintox_datasets import load_clintox

# Only for debug!

np.random.seed(123)

g = tf.Graph()

sess = tf.Session(graph=g)

K.set_session(sess)

with g.as_default():

  # Load clintox dataset

  n_features = 1024

  clintox_tasks, clintox_datasets, transformers = load_clintox(

      featurizer='GraphConv', split='random')

  train_dataset, valid_dataset, test_dataset = clintox_datasets

  # Fit models

  metric = dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean,

                             mode="classification")

  # Do setup required for tf/keras models

  # Number of features on conv-mols

  n_feat = 75

  # Batch size of models

  batch_size = 50

  graph_model = dc.nn.SequentialGraph(n_feat)

  graph_model.add(dc.nn.GraphConv(64, 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(64, 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(128, 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.MultitaskGraphClassifier(sess, graph_model,

                                               len(clintox_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)

    print("Train scores")

    print(train_scores)

    print("Validation scores")

    print(valid_scores)

"""

Script that trains multitask models on clintox dataset.

@author Caleb Geniesse

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

import numpy as np

import deepchem as dc

from clintox_datasets import load_clintox

# Only for debug!

np.random.seed(123)

# Load clintox dataset

n_features = 1024

clintox_tasks, clintox_datasets, transformers = load_clintox(split='random')

train_dataset, valid_dataset, test_dataset = clintox_datasets

# Fit models

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

model = dc.models.TensorflowMultiTaskClassifier(len(clintox_tasks), 

                                                n_features,

                                                layer_sizes=[1000],

                                                dropouts=[.25],

                                                learning_rate=0.001,

                                                batch_size=50)

# Fit trained model

model.fit(train_dataset)

model.save()

print("Evaluating model")

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

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

print("Train scores")

print(train_scores)

print("Validation scores")

print(valid_scores)