Some cleanup to remove cruft code.

# TODO(rbharath): I believe this is broken. Update it to work with the rest of

# the package.

def fit_multitask_rf(train_data, test_data, task_types):

def fit_multitask_rf(train_data, task_types):

  """Fits a multitask RF model to provided dataset.

  """

  (_, X_train, y_train, W_train), (test, X_train, y_train, W_train) = (

      train_data, test_data) 

  (_, X_train, y_train, _) = train_data

  model = RandomForestClassifier(n_estimators=100, n_jobs=-1,

      class_weight="auto")

  model.fit(X_train, y_train)

    print "Target %s" % target

    (test_ids, Xtest, ytest, wtest) = test_data[target]

    (_, _, ytest_raw, _) = raw_test_data[target]

    print "ytest"

    print ytest

    print "ytest_raw"

    print ytest_raw

    model = models[target]

    results = eval_model(test_ids, Xtest, ytest, ytest_raw, wtest, model, {target: task_types[target]}, 

                         modeltype=modeltype, output_transforms=output_transforms)

    #print results

    all_results[target] = results[target]

    if aucs:

      auc_vals.update(compute_roc_auc_scores(results, task_types))

  ypreds = model_predictions(X, model, len(task_types),

      task_types, modeltype=modeltype)

  results = {}

  print "eval_model()"

  print "Ytrue"

  print Ytrue

  print "Ytrue_raw"

  print Ytrue_raw

  for target_ind, target in enumerate(sorted_targets):

    ytrue_raw, ytrue, ypred = Ytrue_raw[:, target_ind], Ytrue[:, target_ind], ypreds[:, target_ind]

    ypred = undo_transform_outputs(ytrue_raw, ypred, output_transforms)

    #ytrue_trans = undo_transform_outputs(ytrue_raw, ytrue, output_transforms)

    print "ytrue_raw"

    print ytrue_raw

    #print "ytrue_trans"

    #print ytrue_trans

    #results[target] = (ids, np.squeeze(ytrue), np.squeeze(ypred))

    results[target] = (ids, np.squeeze(ytrue_raw), np.squeeze(ypred))

  return results

  print np.shape(train_dict['CANVAS-BACE'][1])

  return train_dict, test_dict 

#def transform_data(data_dict, input_transforms, output_transforms):

#  """Transforms data using specified transforms"""

#  trans_dict = {}

#  for target in data_dict:

#    data = data_dict[target]

#    trans_data = transform_data(data, input_transforms, output_transforms)

#    trans_dict[target] = trans_data

#  return trans_dict

def load_molecules(paths, feature_types=["fingerprints"]):

  """Load dataset fingerprints and return fingerprints.