changes

import tempfile

from deepchem.data import DiskDataset

from deepchem.models import SklearnModel

from deepchem.models import TensorflowMultiTaskRegressor

from deepchem.models import MultiTaskRegressor

from deepchem.dock.pose_scoring import GridPoseScorer

from deepchem.dock.pose_generation import VinaPoseGenerator

from sklearn.ensemble import RandomForestRegressor

    # Fit model on dataset

    pdbbind_tasks = ["-logKd/Ki"]

    n_features = 2052

    model = TensorflowMultiTaskRegressor(

    model = MultiTaskRegressor(

        len(pdbbind_tasks),

        n_features,

        logdir=self.model_dir,

        dropouts=[.25],

        learning_rate=0.0003,

        weight_init_stddevs=[.1],

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

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

  >>> fit_transformers = [dc.trans.CoulombFitTransformer(dataset)]

  >>> model = dc.models.TensorflowMultiTaskFitTransformRegressor(n_tasks, [n_features, n_features],

  >>> model = dc.models.MultiTaskFitTransformRegressor(n_tasks, [n_features, n_features],

  ...     dropouts=[0.], learning_rate=0.003, weight_init_stddevs=[np.sqrt(6)/np.sqrt(1000)],

  ...     batch_size=n_samples, fit_transformers=fit_transformers, n_evals=1)

  n_features after fit_transform: 12