First steps to reload test

from flaky import flaky

from sklearn.ensemble import RandomForestClassifier

from deepchem.molnet.load_function.chembl25_datasets import chembl25_tasks

from deepchem.feat import create_char_to_idx

def test_sklearn_classifier_reload():

  origpred = model.predict(predset)

  reloadpred = reloaded_model.predict(predset)

  assert np.all(origpred == reloadpred)

def test_smiles2vec_reload():

  """Test that smiles2vec models can be saved and reloaded."""

  max_len = 250

  pad_len = 10

  char_to_idx = create_char_to_idx(

      dataset_file, max_len=max_len, smiles_field="smiles")

  feat = dc.feat.SmilesToSeq(

      char_to_idx=char_to_idx, max_len=max_len, pad_len=pad_len)

  n_tasks = 1

  data_points = 10

  mols = ["CCCCCCCC"] * data_points

  X = featurizer(mols)

  y = np.random.randint(0, 2, size=(data_points, n_tasks))

  w = np.ones(shape=(data_points, n_tasks))

  dataset = dc.data.NumpyDataset(X, y, w, mols)

  classsification_metric = dc.metrics.Metric(

      dc.metrics.roc_auc_score, np.mean, mode="classification")

  model_dir = tempfile.mkdtemp()

  model = Smiles2Vec(

      char_to_idx=char_to_idx,

      max_seq_len=max_seq_len,

      use_conv=True,

      n_tasks=n_tasks,

      model_dir=model_dir,

      mode="classification")

  model.fit(dataset, nb_epoch=3)

  ## Reload Trained Model

  #reloaded_model = dc.models.ChemCeption(

  #    n_tasks=n_tasks,

  #    img_spec="engd",

  #    model_dir=model_dir,

  #    mode="classification")

  #reloaded_model.restore()

  ## Check predictions match on random sample

  #predmols = ["CCCC", "CCCCCO", "CCCCC"]

  #Xpred = featurizer(predmols)

  #predset = dc.data.NumpyDataset(Xpred)

  #origpred = model.predict(predset)

  #reloadpred = reloaded_model.predict(predset)

  #assert np.all(origpred == reloadpred)