Commit bf289292 authored by Bharath Ramsundar's avatar Bharath Ramsundar
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Examples

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examples/low_data/README.md

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# Low Data Examples



Training models with low data



TODO(rbharath): Add more details

examples/low_data/__init__.py

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examples/low_data/datasets.py

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"""

Load datasets for Low Data processing.

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals



import os

import sys

import shutil

import tempfile

import numpy as np

import deepchem as dc



def to_numpy_dataset(dataset):

  """Converts dataset to numpy dataset."""

  return dc.data.NumpyDataset(dataset.X, dataset.y, dataset.w, dataset.ids)



def load_tox21_ecfp(num_train=7200):

  """Load Tox21 datasets. Does not do train/test split"""

  # Set some global variables up top

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

  dataset_file = os.path.join(

      current_dir, "../../datasets/tox21.csv.gz")

  # Featurize Tox21 dataset

  print("About to featurize Tox21 dataset.")

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

  tox21_tasks = ['NR-AR', 'NR-AR-LBD', 'NR-AhR', 'NR-Aromatase', 'NR-ER',

                 'NR-ER-LBD', 'NR-PPAR-gamma', 'SR-ARE', 'SR-ATAD5',

                 'SR-HSE', 'SR-MMP', 'SR-p53']



  loader = dc.data.CSVLoader(

      tasks=tox21_tasks, smiles_field="smiles", featurizer=featurizer)

  dataset = loader.featurize(

      dataset_file, shard_size=8192)



  # Initialize transformers 

  transformers = [

      dc.trans.BalancingTransformer(transform_w=True, dataset=dataset)]



  print("About to transform data")

  for transformer in transformers:

    dataset = transformer.transform(dataset)



  return tox21_tasks, dataset, transformers



def load_tox21_convmol(base_dir=None, num_train=7200):

  """Load Tox21 datasets. Does not do train/test split"""

  # Set some global variables up top

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

  dataset_file = os.path.join(

      current_dir, "../../datasets/tox21.csv.gz")



  # Featurize Tox21 dataset

  print("About to featurize Tox21 dataset.")

  featurizer = dc.feat.ConvMolFeaturizer()

  tox21_tasks = ['NR-AR', 'NR-AR-LBD', 'NR-AhR', 'NR-Aromatase', 'NR-ER',

                 'NR-ER-LBD', 'NR-PPAR-gamma', 'SR-ARE', 'SR-ATAD5',

                 'SR-HSE', 'SR-MMP', 'SR-p53']



  loader = dc.data.CSVLoader(

      tasks=tox21_tasks, smiles_field="smiles", featurizer=featurizer)

  dataset = loader.featurize(

      dataset_file, shard_size=8192)



  # Initialize transformers 

  transformers = [

      dc.trans.BalancingTransformer(transform_w=True, dataset=dataset)]



  print("About to transform data")

  for transformer in transformers:

    dataset = transformer.transform(dataset)



  return tox21_tasks, dataset, transformers



def load_muv_ecfp():

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

  # Load MUV dataset

  print("About to load MUV dataset.")

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

  dataset_file = os.path.join(

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

  # Featurize MUV dataset

  print("About to featurize MUV dataset.")

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

  MUV_tasks = sorted(['MUV-692', 'MUV-689', 'MUV-846', 'MUV-859', 'MUV-644',

                      'MUV-548', 'MUV-852', 'MUV-600', 'MUV-810', 'MUV-712',

                      'MUV-737', 'MUV-858', 'MUV-713', 'MUV-733', 'MUV-652',

                      'MUV-466', 'MUV-832'])



  loader = dc.data.CSVLoader(

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

  dataset = loader.featurize(dataset_file)



  # Initialize transformers 

  transformers = [

      dc.trans.BalancingTransformer(transform_w=True, dataset=dataset)]

  print("About to transform data")

  for transformer in transformers:

      dataset = transformer.transform(dataset)



  return MUV_tasks, dataset, transformers



def load_muv_convmol():

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

  # Load MUV dataset

  print("About to load MUV dataset.")

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

  dataset_file = os.path.join(

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

  # Featurize MUV dataset

  print("About to featurize MUV dataset.")

  featurizer = dc.feat.ConvMolFeaturizer()

  MUV_tasks = sorted(['MUV-692', 'MUV-689', 'MUV-846', 'MUV-859', 'MUV-644',

                      'MUV-548', 'MUV-852', 'MUV-600', 'MUV-810', 'MUV-712',

                      'MUV-737', 'MUV-858', 'MUV-713', 'MUV-733', 'MUV-652',

                      'MUV-466', 'MUV-832'])



  loader = dc.data.CSVLoader(

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

  dataset = loader.featurize(dataset_file)



  # Initialize transformers 

  transformers = [

      dc.trans.BalancingTransformer(transform_w=True, dataset=dataset)]

  print("About to transform data")

  for transformer in transformers:

      dataset = transformer.transform(dataset)



  return MUV_tasks, dataset, transformers



def load_sider_ecfp():

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

  # Featurize SIDER dataset

  print("About to featurize SIDER dataset.")

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

  dataset_file = os.path.join(

      current_dir, "../sider/sider.csv.gz")

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



  dataset = dc.utils.save.load_from_disk(dataset_file)

  SIDER_tasks = dataset.columns.values[1:].tolist()

  print("SIDER tasks: %s" % str(SIDER_tasks))

  print("%d tasks in total" % len(SIDER_tasks))





  loader = dc.data.CSVLoader(

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

  dataset = loader.featurize(dataset_file)

  print("%d datapoints in SIDER dataset" % len(dataset))



  # Initialize transformers

  transformers = [

      dc.trans.BalancingTransformer(transform_w=True, dataset=dataset)]

  print("About to transform data")

  for transformer in transformers:

    dataset = transformer.transform(dataset)



  return SIDER_tasks, dataset, transformers



def load_sider_convmol():

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

  # Featurize SIDER dataset

  print("About to featurize SIDER dataset.")

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

  dataset_file = os.path.join(

      current_dir, "../sider/sider.csv.gz")

  featurizer = dc.feat.ConvMolFeaturizer()



  dataset = dc.utils.save.load_from_disk(dataset_file)

  SIDER_tasks = dataset.columns.values[1:].tolist()

  print("SIDER tasks: %s" % str(SIDER_tasks))

  print("%d tasks in total" % len(SIDER_tasks))





  loader = dc.data.CSVLoader(

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

  dataset = loader.featurize(dataset_file)

  print("%d datapoints in SIDER dataset" % len(dataset))



  # Initialize transformers

  transformers = [

      dc.trans.BalancingTransformer(transform_w=True, dataset=dataset)]

  print("About to transform data")

  for transformer in transformers:

    dataset = transformer.transform(dataset)



  return SIDER_tasks, dataset, transformers

examples/low_data/muv_graph_conv_one_fold.py

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"""

Train low-data MUV models with graph-convolution. Test last fold only.

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals



import tempfile

import numpy as np

import tensorflow as tf

import deepchem as dc

from datasets import load_muv_convmol



# 4-fold splits

K = 4

# num positive/negative ligands

n_pos = 10

n_neg = 10

# 10 trials on test-set

n_trials = 20



muv_tasks, dataset, transformers = load_muv_convmol()



# Define metric

metric = dc.metrics.Metric(dc.metrics.roc_auc_score, mode="classification")



task_splitter = dc.splits.TaskSplitter()

fold_datasets = task_splitter.k_fold_split(dataset, K)



train_folds = fold_datasets[:-1]

train_dataset = dc.splits.merge_fold_datasets(train_folds)

test_dataset = fold_datasets[-1]



# Get supports on test-set

support_generator = dc.data.SupportGenerator(test_dataset, n_pos, n_neg,

                                             n_trials)



# Compute accuracies

task_scores = {task: [] for task in range(len(test_dataset.get_task_names()))}



for trial_num, (task, support) in enumerate(support_generator):

  print("Starting trial %d" % trial_num)



  # 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, n_feat, activation='relu'))

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

  graph_model.add(dc.nn.GraphConv(128, 64, activation='relu'))

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

  graph_model.add(dc.nn.GraphConv(64, 128, activation='relu'))

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

  graph_model.add(dc.nn.Dense(128, 64, activation='tanh'))

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



  model = dc.models.MultitaskGraphClassifier(

      graph_model,

      1,

      n_feat,

      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(support, nb_epoch=10)



  # Test model

  task_dataset = dc.data.get_task_dataset_minus_support(test_dataset, support,

                                                        task)

  y_pred = model.predict(task_dataset)

  score = metric.compute_metric(task_dataset.y, y_pred, task_dataset.w)

  print("Score on task %s is %s" % (str(task), str(score)))

  task_scores[task].append(score)



# Join information for all tasks.

mean_task_scores = {}

std_task_scores = {}

for task in range(len(test_dataset.get_task_names())):

  mean_task_scores[task] = np.mean(np.array(task_scores[task]))

  std_task_scores[task] = np.std(np.array(task_scores[task]))



print("Mean scores")

print(mean_task_scores)

print("Standard Deviations")

print(std_task_scores)

print("Median of Mean Scores")

print(np.median(np.array(mean_task_scores.values())))

examples/low_data/muv_rf_one_fold.py

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"""

Train low-data MUV models with random forests. Test last fold only.

"""

from __future__ import print_function

from __future__ import division

from __future__ import unicode_literals



import tempfile

import numpy as np

import deepchem as dc

from datasets import load_muv_ecfp

from sklearn.ensemble import RandomForestClassifier



# 4-fold splits

K = 4

# num positive/negative ligands

n_pos = 10

n_neg = 10

# 10 trials on test-set

n_trials = 20



tox21_tasks, dataset, transformers = load_muv_ecfp()



# Define metric

metric = dc.metrics.Metric(dc.metrics.roc_auc_score, mode="classification")



task_splitter = dc.splits.TaskSplitter()

fold_datasets = task_splitter.k_fold_split(dataset, K)



train_folds = fold_datasets[:-1] 

train_dataset = dc.splits.merge_fold_datasets(train_folds)

test_dataset = fold_datasets[-1]



# Get supports on test-set

support_generator = dc.data.SupportGenerator(

    test_dataset, n_pos, n_neg, n_trials)



# Compute accuracies

task_scores = {task: [] for task in range(len(test_dataset.get_task_names()))}

for (task, support) in support_generator:

  # Train model on support

  sklearn_model = RandomForestClassifier(

      class_weight="balanced", n_estimators=100)

  model = dc.models.SklearnModel(sklearn_model)

  model.fit(support)



  # Test model

  task_dataset = dc.data.get_task_dataset_minus_support(

      test_dataset, support, task)

  y_pred = model.predict_proba(task_dataset)

  score = metric.compute_metric(

      task_dataset.y, y_pred, task_dataset.w)

  print("Score on task %s is %s" % (str(task), str(score)))

  task_scores[task].append(score)



# Join information for all tasks.

mean_task_scores = {}

std_task_scores = {}

for task in range(len(test_dataset.get_task_names())):

  mean_task_scores[task] = np.mean(np.array(task_scores[task]))

  std_task_scores[task] = np.std(np.array(task_scores[task]))



print("Mean scores")

print(mean_task_scores)

print("Standard Deviations")

print(std_task_scores)

print("Median of Mean Scores")

print(np.median(np.array(mean_task_scores.values())))

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