nci data and rf & dnn scripts

"""

NCI dataset loader.

Original Author - Bharath Ramsundar

Author - Aneesh Pappu

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

import os

import sys

import numpy as np

import shutil

from deepchem.utils.save import load_from_disk

from deepchem.datasets import Dataset

from deepchem.featurizers.featurize import DataFeaturizer

from deepchem.featurizers.fingerprints import CircularFingerprint

from deepchem.transformers import BalancingTransformer

def load_nci(base_dir, reload=True):

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

  # Set some global variables up top

  #reload = True

  verbosity = "high"

  model = "logistic"

  regen = False

  # Create some directories for analysis

  # The base_dir holds the results of all analysis

  if not reload:

    if os.path.exists(base_dir):

      print("deleting dir in datasets.py")

      print(base_dir)

      shutil.rmtree(base_dir)

  if not os.path.exists(base_dir):

    os.makedirs(base_dir)

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

  #Make directories to store the raw and featurized datasets.

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

  # Load nci dataset

  print("About to load NCI dataset.")

  dataset_file = os.path.join(

      current_dir, "../../datasets/nci.csv.gz")

  print(dataset_file)

  dataset = load_from_disk(dataset_file)

  print("Columns of dataset: %s" % str(dataset.columns.values))

  print("Number of examples in dataset: %s" % str(dataset.shape[0]))

  # Featurize nci dataset

  print("About to featurize nci dataset.")

  featurizers = [CircularFingerprint(size=1024)]

  #was sorted list originally in muv_datasets.py, but csv is ordered so removed

  all_nci_tasks = (['CCRF-CEM', 'HL-60(TB)', 'K-562', 'MOLT-4', 'RPMI-8226',

                          'SR', 'A549/ATCC', 'EKVX', 'HOP-62', 'HOP-92', 'NCI-H226',

                          'NCI-H23', 'NCI-H322M', 'NCI-H460', 'NCI-H522', 'COLO 205',

                          'HCC-2998', 'HCT-116', 'HCT-15', 'HT29', 'KM12', 'SW-620',

                          'SF-268', 'SF-295', 'SF-539', 'SNB-19', 'SNB-75', 'U251',

                          'LOX IMVI', 'MALME-3M', 'M14', 'MDA-MB-435', 'SK-MEL-2',

                          'SK-MEL-28', 'SK-MEL-5', 'UACC-257', 'UACC-62', 'IGR-OV1',

                          'OVCAR-3', 'OVCAR-4', 'OVCAR-5', 'OVCAR-8', 'NCI/ADR-RES',

                          'SK-OV-3', '786-0', 'A498', 'ACHN', 'CAKI-1', 'RXF 393',

                          'SN12C', 'TK-10', 'UO-31', 'PC-3', 'DU-145', 'MCF7',

                          'MDA-MB-231/ATCC', 'MDA-MB-468', 'HS 578T', 'MDA-N', 'BT-549'])

  featurizer = DataFeaturizer(tasks=all_nci_tasks,

                              smiles_field="smiles",

                              featurizers=featurizers,

                              verbosity=verbosity)

  if not reload or not os.path.exists(data_dir):

    dataset = featurizer.featurize(dataset_file, data_dir)

    regen = True

  else:

    dataset = Dataset(data_dir, reload=True)

  # Initialize transformers

  transformers = [

      BalancingTransformer(transform_w=True, dataset=dataset)]

  if regen:

    print("About to transform data")

    for transformer in transformers:

        transformer.transform(dataset)

  return all_nci_tasks, dataset, transformers

"""

Script that trains Sklearn multitask models on nci dataset.

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals

import os

import numpy as np

import shutil

from sklearn.linear_model import LogisticRegression

from sklearn.ensemble import RandomForestClassifier

from deepchem.datasets import Dataset

from deepchem.models.multitask import SingletaskToMultitask

from deepchem import metrics

from deepchem.metrics import Metric

from deepchem.models.sklearn_models import SklearnModel

from deepchem.utils.evaluate import Evaluator

from deepchem.datasets.nci_datasets import load_nci

np.random.seed(123)

# Set some global variables up top

reload = False

verbosity = "high"

#base_data_dir = "/scratch/users/rbharath/muv"

base_data_dir = "/home/apappu/deepchem/examples/nci/dataset"

nci_tasks, dataset, transformers = load_nci(

    base_data_dir, reload=reload)

print("len(dataset)")

print(len(dataset))

#base_dir = "/scratch/users/rbharath/muv_analysis"

base_dir = "/home/apappu/deepchem/examples/nci/"

if os.path.exists(base_dir):

  shutil.rmtree(base_dir)

if not os.path.exists(base_dir):

  os.makedirs(base_dir)

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

valid_dir = os.path.join(base_dir, "valid_dataset")

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

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

print("About to perform train/valid/test split.")

num_train = .8 * len(dataset)

X, y, w, ids = dataset.to_numpy()

num_tasks = 17

nci_tasks = nci_tasks[:num_tasks]

print("Using following tasks")

print(nci_tasks)

X_train, X_valid = X[:num_train], X[num_train:]

y_train, y_valid = y[:num_train, :num_tasks], y[num_train:, :num_tasks]

w_train, w_valid = w[:num_train, :num_tasks], w[num_train:, :num_tasks]

ids_train, ids_valid = ids[:num_train], ids[num_train:]

if os.path.exists(train_dir):

  shutil.rmtree(train_dir)

train_dataset = Dataset.from_numpy(train_dir, X_train, y_train,

                                   w_train, ids_train, nci_tasks)

if os.path.exists(valid_dir):

  shutil.rmtree(valid_dir)

valid_dataset = Dataset.from_numpy(valid_dir, X_valid, y_valid,

                                   w_valid, ids_valid, nci_tasks)

# Fit Logistic Regression models

nci_task_types = {task: "classification" for task in nci_tasks}

classification_metric = Metric(metrics.roc_auc_score, np.mean,

                               verbosity=verbosity,

                               mode="classification")

params_dict = {

    "batch_size": None,

    "data_shape": train_dataset.get_data_shape(),

}

if os.path.exists(model_dir):

  shutil.rmtree(model_dir)

os.makedirs(model_dir)

def model_builder(tasks, task_types, model_params, model_dir, verbosity=None):

  return SklearnModel(tasks, task_types, model_params, model_dir,

                      #model_instance=LogisticRegression(class_weight="balanced"),

                      model_instance=RandomForestClassifier(

                          class_weight="balanced",

                          n_estimators=500),

                      verbosity=verbosity)

model = SingletaskToMultitask(nci_tasks, nci_task_types, params_dict, model_dir,

                              model_builder, verbosity=verbosity)

# Fit trained model

model.fit(train_dataset)

model.save()

train_evaluator = Evaluator(model, train_dataset, transformers, verbosity=verbosity)

train_scores = train_evaluator.compute_model_performance([classification_metric])

print("Train scores")

print(train_scores)

valid_evaluator = Evaluator(model, valid_dataset, transformers, verbosity=verbosity)

valid_scores = valid_evaluator.compute_model_performance([classification_metric])

print("Validation scores")

print(valid_scores)