Initial delaney solubility models.

"""

Delaney dataset loader.

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

import os

import numpy as np

import shutil

import deepchem as dc

def load_delaney(featurizer='ECFP'):

  """Load delaney datasets."""

  # Featurize Delaney dataset

  print("About to featurize Delaney dataset.")

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

  dataset_file = os.path.join(

      current_dir, "../../datasets/delaney-processed.csv")

  delaney_tasks = ['measured log solubility in mols per litre']

  if featurizer == 'ECFP':

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

  elif featurizer == 'GraphConv':

    featurizer_func = dc.feat.ConvMolFeaturizer()

  loader = dc.load.DataLoader(

      tasks=delaney_tasks, smiles_field="smiles",

      featurizer=featurizer_func, verbosity = 'high')

  dataset = loader.featurize(

      dataset_file, shard_size=8192)

  # Initialize transformers 

  transformers = [

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

  print("About to transform data")

  for transformer in transformers:

      dataset = transformer.transform(dataset)

  splitter = dc.splits.IndexSplitter()

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

      compute_feature_statistics=False)

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

"""

Script that trains multitask models on Delaney dataset.

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

import os

import shutil

import numpy as np

import deepchem as dc

from delaney_dataset import load_delaney

# Only for debug!

np.random.seed(123)

# Load Delaney dataset

n_features = 1024

delaney_tasks, delaney_datasets, transformers = load_delaney()

train_dataset, valid_dataset, test_dataset = delaney_datasets

# Fit models

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

model = dc.models.TensorflowMultiTaskRegressor(

    len(delaney_tasks), n_features, layer_sizes=[1000], dropouts=[.25],

    learning_rate=0.001, batch_size=50, verbosity="high")

# 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)