Commit 29cab76f authored by Bharath Ramsundar's avatar Bharath Ramsundar
Browse files

Add tasks parameter to models to ensure tasks retain ordering.

parent 57858df9

deepchem/datasets/__init__.py

+5 −7
Original line number Diff line number Diff line
@@ -125,7 +125,7 @@ class Dataset(object): 
    """

    if not len(self.metadata_df):

      raise ValueError("No data in dataset.")

    return sorted(self.metadata_df.iterrows().next()[1]['task_names'])

    return self.metadata_df.iterrows().next()[1]['task_names']



  def get_data_shape(self):

    """
@@ -309,8 +309,7 @@ def write_dataset_single(val, data_dir, feature_types, tasks): 
  # TODO(rbharath): This is a hack. clean up.

  if not len(df):

    return None

  sorted_tasks = sorted(tasks)

  ids, X, y, w = _df_to_numpy(df, feature_types, sorted_tasks)

  ids, X, y, w = _df_to_numpy(df, feature_types, tasks)

  X_sums, X_sum_squares, X_n = compute_sums_and_nb_sample(X)

  y_sums, y_sum_squares, y_n = compute_sums_and_nb_sample(y, w)
@@ -346,7 +345,7 @@ def write_dataset_single(val, data_dir, feature_types, tasks): 
  save_to_disk(ids, out_ids)

  # TODO(rbharath): Should X be saved to out_X_transformed as well? Since

  # itershards expects to loop over X-transformed? (Ditto for y/w)

  return([df_file, sorted_tasks, out_ids, out_X, out_X_transformed, out_y,

  return([df_file, tasks, out_ids, out_X, out_X_transformed, out_y,

          out_y_transformed, out_w, out_w_transformed,

          out_X_sums, out_X_sum_squares, out_X_n,

          out_y_sums, out_y_sum_squares, out_y_n])
@@ -360,10 +359,9 @@ def _df_to_numpy(df, feature_types, tasks): 
        "Featurized data does not support requested feature_types.")

  # perform common train/test split across all tasks

  n_samples = df.shape[0]

  sorted_tasks = sorted(tasks)

  n_tasks = len(sorted_tasks)

  n_tasks = len(tasks)

  n_features = None

  y = df[sorted_tasks].values

  y = df[tasks].values

  y = np.reshape(y, (n_samples, n_tasks))

  w = np.ones((n_samples, n_tasks))

  missing = np.zeros_like(y).astype(int)

deepchem/datasets/tests/__init__.py

+9 −5
Original line number Diff line number Diff line
@@ -55,12 +55,14 @@ class TestDatasetAPI(TestSplitAPI): 
    complex_featurizers = []

    input_transformer_classes = []

    output_transformer_classes = []

    task_types = {"log-solubility": "regression"}

    tasks = ["log-solubility"]

    task_type = "regression"

    task_types = {task: task_type for task in tasks}

    input_file = "example.csv"

    return self._create_dataset(

        compound_featurizers, complex_featurizers,

        input_transformer_classes, output_transformer_classes,

        input_file, task_types.keys())

        input_file, tasks)



  def _load_classification_data(self):

    """Loads classification data from example.csv"""
@@ -68,12 +70,14 @@ class TestDatasetAPI(TestSplitAPI): 
    complex_featurizers = []

    input_transformer_classes = []

    output_transformer_classes = []

    task_types = {"outcome": "classification"}

    tasks = ["outcome"]

    task_type = "classification"

    task_types = {task: task_type for task in tasks}

    input_file = "example_classification.csv"

    return self._create_dataset(

        compound_featurizers, complex_featurizers,

        input_transformer_classes, output_transformer_classes,

        input_file, task_types.keys())

        input_file, tasks)



  def _load_multitask_data(self):

    """Load example multitask data."""
@@ -89,5 +93,5 @@ class TestDatasetAPI(TestSplitAPI): 
    return self._create_dataset(

        compound_featurizers, complex_featurizers,

        input_transformer_classes, output_transformer_classes,

        input_file, task_types.keys())

        input_file, tasks)

deepchem/featurizers/featurize.py

+3 −3
Original line number Diff line number Diff line
@@ -91,7 +91,7 @@ class DataFeaturizer(object): 
      raise ValueError("tasks must be a list.")

    assert verbosity in [None, "low", "high"]

    self.verbosity = verbosity

    self.sorted_tasks = sorted(tasks)

    self.tasks = tasks

    self.smiles_field = smiles_field

    self.split_field = split_field

    if id_field is None:
@@ -189,7 +189,7 @@ class DataFeaturizer(object): 
    else:

      raise ValueError("Unrecognized input_type")

    if self.threshold is not None:

      for task in self.sorted_tasks:

      for task in self.tasks:

        raw = _process_field(data[task])

        if not isinstance(raw, float):

          raise ValueError("Cannot threshold non-float fields.")
@@ -201,7 +201,7 @@ class DataFeaturizer(object): 
    df = pd.DataFrame(ori_df[[self.id_field]])

    df.columns = ["mol_id"]

    df["smiles"] = ori_df[[self.smiles_field]]

    for task in self.sorted_tasks:

    for task in self.tasks:

      df[task] = ori_df[[task]]

    if self.user_specified_features is not None:

      for feature in self.user_specified_features:

deepchem/featurizers/tests/test_featurized_samples.py

+16 −8
Original line number Diff line number Diff line
@@ -78,11 +78,13 @@ class TestFeaturizedSamples(unittest.TestCase): 
    input_transforms = []

    output_transforms = ["normalize"]

    model_params = {}

    task_types = {"log-solubility": "regression"}

    tasks = ["log-solubility"]

    task_type = "regression"

    task_types = {task: task_type for task in tasks}

    input_file = "../../models/test/example.csv"

    train_samples, valid_samples, test_samples = (

        self._featurize_train_valid_test_split(

            splittype, input_file, task_types.keys(), frac_train=.8,

            splittype, input_file, tasks, frac_train=.8,

            frac_valid=.1, frac_test=.1))

    assert len(train_samples) == 8

    assert len(valid_samples) == 1
@@ -94,11 +96,13 @@ class TestFeaturizedSamples(unittest.TestCase): 
    input_transforms = []

    output_transforms = ["normalize"]

    model_params = {}

    task_types = {"log-solubility": "regression"}

    tasks = ["log-solubility"]

    task_type = "regression"

    task_types = {task: task_type for task in tasks}

    input_file = "../../models/test/example.csv"

    train_samples, test_samples = (

        self._featurize_train_valid_test_split(

            splittype, input_file, task_types.keys(), frac_train=.8,

            splittype, input_file, tasks), frac_train=.8,

            frac_valid=0, frac_test=.2))

    assert len(train_samples) == 8

    assert len(test_samples) == 2
@@ -109,11 +113,13 @@ class TestFeaturizedSamples(unittest.TestCase): 
    input_transforms = []

    output_transforms = ["normalize"]

    model_params = {}

    task_types = {"log-solubility": "regression"}

    tasks = ["log-solubility"]

    task_type = "regression"

    task_types = {task: task_type for task in tasks}

    input_file = "../../models/test/example.csv"

    train_samples, valid_samples, test_samples = (

        self._featurize_train_valid_test_split(

            splittype, input_file, task_types.keys(), frac_train=.8,

            splittype, input_file, tasks, frac_train=.8,

            frac_valid=.1, frac_test=.1))

    assert len(train_samples) == 8

    assert len(valid_samples) == 1
@@ -125,11 +131,13 @@ class TestFeaturizedSamples(unittest.TestCase): 
    input_transforms = []

    output_transforms = ["normalize"]

    model_params = {}

    task_types = {"log-solubility": "regression"}

    tasks = ["log-solubility"]

    task_type = "regression"

    task_types = {task: task_type for task in tasks}

    input_file = "../../models/test/example.csv"

    train_samples, test_samples = (

        self._featurize_train_valid_test_split(

            splittype, input_file, task_types.keys(), frac_train=.8,

            splittype, input_file, tasks, frac_train=.8,

            frac_valid=0, frac_test=.2))

    assert len(train_samples) == 8

    assert len(test_samples) == 2

deepchem/hyperparameters/__init__.py

+7 −5
Original line number Diff line number Diff line
@@ -15,8 +15,9 @@ class HyperparamOpt(object): 
  Provides simple hyperparameter search capabilities.

  """



  def __init__(self, model_class, task_types, fit_transformers=None, verbosity=None):

  def __init__(self, model_class, tasks, task_types, fit_transformers=None, verbosity=None):

    self.model_class = model_class

    self.tasks = tasks

    self.task_types = task_types

    self.fit_transformers = fit_transformers

    assert verbosity in [None, "low", "high"]
@@ -63,11 +64,12 @@ class HyperparamOpt(object): 
        model_dir = tempfile.mkdtemp()

      #TODO(JG) Fit transformers for TF models

      if self.fit_transformers:

        model = self.model_class(self.task_types, model_params, model_dir,

                                 fit_transformers=self.fit_transformers,

                                 verbosity=self.verbosity)

        model = self.model_class(

            self.tasks, self.task_types, model_params, model_dir,

            fit_transformers=self.fit_transformers, verbosity=self.verbosity)

      else:

        model = self.model_class(self.task_types, model_params, model_dir,

        model = self.model_class(

            self.tasks, self.task_types, model_params, model_dir,

            verbosity=self.verbosity)

        

      model.fit(train_dataset)

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