Commit 0af7a051 authored by Ubuntu's avatar Ubuntu
Browse files

yapfed lots of stuff

parent c94913e0

deepchem/data/supports.py

+40 −32
Original line number Diff line number Diff line
@@ -9,6 +9,7 @@ import time 
import numpy as np

from deepchem.data import NumpyDataset





def remove_dead_examples(dataset):

  """Removes compounds with no weight.
@@ -28,6 +29,7 @@ def remove_dead_examples(dataset): 


  return NumpyDataset(X, y, w, ids)





def dataset_difference(dataset, remove):

  """Removes the compounds in remove from dataset.
@@ -39,8 +41,9 @@ def dataset_difference(dataset, remove): 
    Dataset whose overlap will be removed.

  """

  remove_ids = set(remove.ids)

  keep_inds = [ind for ind in range(len(dataset))

               if dataset.ids[ind] not in remove_ids]

  keep_inds = [

      ind for ind in range(len(dataset)) if dataset.ids[ind] not in remove_ids

  ]



  # Remove support indices

  X = dataset.X[keep_inds]
@@ -50,6 +53,7 @@ def dataset_difference(dataset, remove): 


  return NumpyDataset(X, y, w, ids)





def get_task_dataset_minus_support(dataset, support, task):

  """Gets data for specified task, minus support points.
@@ -66,8 +70,9 @@ def get_task_dataset_minus_support(dataset, support, task): 
    Task number of task to select.

  """

  support_ids = set(support.ids)

  non_support_inds = [ind for ind in range(len(dataset))

                      if dataset.ids[ind] not in support_ids]

  non_support_inds = [

      ind for ind in range(len(dataset)) if dataset.ids[ind] not in support_ids

  ]



  # Remove support indices

  X = dataset.X[non_support_inds]
@@ -85,6 +90,7 @@ def get_task_dataset_minus_support(dataset, support, task): 


  return NumpyDataset(X_task, y_task, w_task, ids_task)





def get_task_dataset(dataset, task):

  """Selects out entries for a particular task."""

  X, y, w, ids = dataset.X, dataset.y, dataset.w, dataset.ids
@@ -98,6 +104,7 @@ def get_task_dataset(dataset, task): 


  return NumpyDataset(X_task, y_task, w_task, ids_task)





def get_task_test(dataset, n_episodes, n_test, task, log_every_n=50):

  """Gets test set from specified task.
@@ -135,6 +142,7 @@ def get_task_test(dataset, n_episodes, n_test, task, log_every_n=50): 
    tests.append(NumpyDataset(X_batch, y_batch, w_batch, ids_batch))

  return tests





def get_single_task_test(dataset, batch_size, task, replace=True):

  """Gets test set from specified task.
@@ -156,7 +164,6 @@ def get_single_task_test(dataset, batch_size, task, replace=True): 
  return NumpyDataset(X_batch, y_batch, w_batch, ids_batch)







def get_single_task_support(dataset, n_pos, n_neg, task, replace=True):

  """Generates one support set purely for specified task.
@@ -180,6 +187,7 @@ def get_single_task_support(dataset, n_pos, n_neg, task, replace=True): 
  """

  return get_task_support(dataset, 1, n_pos, n_neg, task)[0]





def get_task_support(dataset, n_episodes, n_pos, n_neg, task, log_every_n=50):

  """Generates one support set purely for specified task.
@@ -221,26 +229,23 @@ def get_task_support(dataset, n_episodes, n_pos, n_neg, task, log_every_n=50): 
    # Handle one-d vs. non one-d feature matrices

    one_dimensional_features = (len(dataset.X.shape) == 1)

    if not one_dimensional_features:

      X = np.vstack(

          [dataset.X[pos_inds], dataset.X[neg_inds]])

      X = np.vstack([dataset.X[pos_inds], dataset.X[neg_inds]])

    else:

      X = np.concatenate(

          [dataset.X[pos_inds], dataset.X[neg_inds]])

    y = np.concatenate(

        [dataset.y[pos_inds, task], dataset.y[neg_inds, task]])

    w = np.concatenate(

        [dataset.w[pos_inds, task], dataset.w[neg_inds, task]])

    ids = np.concatenate(

        [dataset.ids[pos_inds], dataset.ids[neg_inds]])

      X = np.concatenate([dataset.X[pos_inds], dataset.X[neg_inds]])

    y = np.concatenate([dataset.y[pos_inds, task], dataset.y[neg_inds, task]])

    w = np.concatenate([dataset.w[pos_inds, task], dataset.w[neg_inds, task]])

    ids = np.concatenate([dataset.ids[pos_inds], dataset.ids[neg_inds]])

    supports.append(NumpyDataset(X, y, w, ids))

  return supports





class EpisodeGenerator(object):

  """Generates (support, test) pairs for episodic training.



  Precomputes all (support, test) pairs at construction. Allows to reduce

  overhead from computation.

  """



  def __init__(self, dataset, n_pos, n_neg, n_test, n_episodes_per_task):

    """

    Parameters
@@ -268,10 +273,10 @@ class EpisodeGenerator(object): 
    self.task_episodes = {}



    for task in range(self.n_tasks):

      task_supports = get_task_support(

          self.dataset, n_episodes_per_task, n_pos, n_neg, task)

      task_tests = get_task_test(

          self.dataset, n_episodes_per_task, n_test, task)

      task_supports = get_task_support(self.dataset, n_episodes_per_task, n_pos,

                                       n_neg, task)

      task_tests = get_task_test(self.dataset, n_episodes_per_task, n_test,

                                 task)

      self.task_episodes[task] = (task_supports, task_tests)



    # Init the iterator
@@ -280,8 +285,8 @@ class EpisodeGenerator(object): 
    self.task_num = 0

    self.trial_num = 0

    time_end = time.time()

    print("Constructing EpisodeGenerator took %s seconds"

          % str(time_end-time_start))

    print("Constructing EpisodeGenerator took %s seconds" %

          str(time_end - time_start))



  def __iter__(self):

    return self
@@ -317,6 +322,7 @@ class SupportGenerator(object): 
  Iterates over tasks and trials. For each trial, picks one support from

  each task, and returns in a randomized order

  """



  def __init__(self, dataset, n_pos, n_neg, n_trials):

    """

    Parameters
@@ -360,7 +366,10 @@ class SupportGenerator(object): 
      task = self.perm_tasks[self.task_num]  # Get id from permutation

      #support = self.supports[task][self.trial_num]

      support = get_single_task_support(

          self.dataset, n_pos=self.n_pos, n_neg=self.n_neg, task=task,

          self.dataset,

          n_pos=self.n_pos,

          n_neg=self.n_neg,

          task=task,

          replace=False)

      # Increment and update logic

      self.task_num += 1
@@ -372,4 +381,3 @@ class SupportGenerator(object): 
      return (task, support)



  __next__ = next  # Python 3.X compatibility

deepchem/data/tests/test_drop.py

+2 −2
Original line number Diff line number Diff line
@@ -6,6 +6,7 @@ import deepchem as dc 
import numpy as np

from sklearn.ensemble import RandomForestClassifier





class TestDrop(unittest.TestCase):

  """

  Test how loading of malformed compounds is handled.
@@ -21,8 +22,7 @@ class TestDrop(unittest.TestCase): 


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

    print("About to load emols dataset.")

    dataset_file = os.path.join(

        current_dir, "mini_emols.csv")

    dataset_file = os.path.join(current_dir, "mini_emols.csv")



    # Featurize emols dataset

    print("About to featurize datasets.")

deepchem/data/tests/test_reload.py

+8 −8
Original line number Diff line number Diff line
@@ -74,13 +74,13 @@ class TestReload(unittest.TestCase): 
    dataset_file = os.path.join(current_dir,

                                "../../../datasets/mini_muv.csv.gz")

    print("Running experiment for first time without reload.")

    (len_train, len_valid, len_test) = self._run_muv_experiment(dataset_file,

                                                                reload)

    (len_train, len_valid, len_test) = self._run_muv_experiment(

        dataset_file, reload)



    print("Running experiment for second time with reload.")

    reload = True

    (len_reload_train, len_reload_valid, len_reload_test) = (

        self._run_muv_experiment(dataset_file, reload))

    (len_reload_train, len_reload_valid,

     len_reload_test) = (self._run_muv_experiment(dataset_file, reload))

    assert len_train == len_reload_train

    assert len_valid == len_reload_valid

    assert len_test == len_reload_valid
@@ -92,12 +92,12 @@ class TestReload(unittest.TestCase): 
    dataset_file = os.path.join(current_dir,

                                "../../../datasets/mini_muv.csv.gz")

    print("Running experiment for first time with reload.")

    (len_train, len_valid, len_test) = self._run_muv_experiment(dataset_file,

                                                                reload)

    (len_train, len_valid, len_test) = self._run_muv_experiment(

        dataset_file, reload)



    print("Running experiment for second time with reload.")

    (len_reload_train, len_reload_valid, len_reload_test) = (

        self._run_muv_experiment(dataset_file, reload))

    (len_reload_train, len_reload_valid,

     len_reload_test) = (self._run_muv_experiment(dataset_file, reload))

    assert len_train == len_reload_train

    assert len_valid == len_reload_valid

    assert len_test == len_reload_valid

deepchem/data/tests/test_support_generator.py

+2 −2
Original line number Diff line number Diff line
@@ -372,8 +372,8 @@ class TestSupports(unittest.TestCase): 
                                                 n_trials)



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

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

                                                            task)

      task_dataset = dc.data.get_task_dataset_minus_support(

          dataset, support, task)



      task_y = dataset.y[:, task]

      task_w = dataset.w[:, task]

deepchem/metrics/tests/metrics_test.py

+2 −2
Original line number Diff line number Diff line
@@ -43,8 +43,8 @@ class MetricsTest(googletest.TestCase): 
  def test_one_hot(self):

    y = np.array([0, 0, 1, 0, 1, 1, 0])

    y_hot = metrics.to_one_hot(y)

    expected = np.array(

        [[1, 0], [1, 0], [0, 1], [1, 0], [0, 1], [0, 1], [1, 0]])

    expected = np.array([[1, 0], [1, 0], [0, 1], [1, 0], [0, 1], [0, 1], [1,

                                                                          0]])

    yp = metrics.from_one_hot(y_hot)

    assert np.array_equal(expected, y_hot)

    assert np.array_equal(y, yp)

CRA Git | Maintained and supported by SUSTech CRA and CCSE