Attempt at fixing failing doctest

* We match against doctest's :code:`...` wildcard on code where output is usually ignored

* We often use threshold assertions (e.g: :code:`score['mean-pearson_r2_score'] > 0.92`), as this is what matters for model training code.

SAMPL (FreeSolv)

Delaney (ESOL)

----------------

Examples of training models on the SAMPL(FreeSolv) dataset included in `MoleculeNet <./moleculenet.html>`_.

Examples of training models on the Delaney (ESOL) dataset included in `MoleculeNet <./moleculenet.html>`_.

We'll be using its :code:`smiles` field to train models to predict its experimentally measured solvation energy (:code:`expt`).

MultitaskRegressor

^^^^^^^^^^^^^^^^^^

First, we'll load the dataset with :func:`load_sampl() <deepchem.molnet.load_sampl>` and fit a :class:`MultitaskRegressor <deepchem.models.MultitaskRegressor>`:

First, we'll load the dataset with :func:`load_delaney() <deepchem.molnet.load_delaney>` and fit a :class:`MultitaskRegressor <deepchem.models.MultitaskRegressor>`:

.. doctest:: sampl

.. doctest:: delaney

    >>> seed_all()

    >>> # Load SAMPL dataset with default 'index' splitting

    >>> SAMPL_tasks, SAMPL_datasets, transformers = dc.molnet.load_sampl()

    >>> SAMPL_tasks

    ['expt']

    >>> train_dataset, valid_dataset, test_dataset = SAMPL_datasets

    >>> # Load dataset with default 'scaffold' splitting

    >>> tasks, datasets, transformers = dc.molnet.load_delaney()

    >>> tasks

    ['measured log solubility in mols per litre']

    >>> train_dataset, valid_dataset, test_dataset = datasets

    >>>

    >>> # We want to know the pearson R squared score, averaged across tasks

    >>> avg_pearson_r2 = dc.metrics.Metric(dc.metrics.pearson_r2_score, np.mean)

    >>>

    >>> # We'll train a multitask regressor (fully connected network)

    >>> model = dc.models.MultitaskRegressor(

    ...     len(SAMPL_tasks),

    ...     len(tasks),

    ...     n_features=1024,

    ...     layer_sizes=[1000],

    ...     dropouts=[.25],

    ...     learning_rate=0.001,

    ...     batch_size=50)

    ...     layer_sizes=[500])

    >>>

    >>> model.fit(train_dataset)

    0...

    >>>

    >>> # We now evaluate our fitted model on our training and validation sets

    >>> train_scores = model.evaluate(train_dataset, [avg_pearson_r2], transformers)

    >>> assert train_scores['mean-pearson_r2_score'] > 0.9, train_scores

    >>> assert train_scores['mean-pearson_r2_score'] > 0.7, train_scores

    >>>

    >>> valid_scores = model.evaluate(valid_dataset, [avg_pearson_r2], transformers)

    >>> assert valid_scores['mean-pearson_r2_score'] > 0.7, valid_scores

    >>> assert valid_scores['mean-pearson_r2_score'] > 0.3, valid_scores

GraphConvModel

^^^^^^^^^^^^^^

The default `featurizer <./featurizers.html>`_ for SAMPL is :code:`ECFP`, short for

The default `featurizer <./featurizers.html>`_ for Delaney is :code:`ECFP`, short for

`"Extended-connectivity fingerprints." <./featurizers.html#circularfingerprint>`_

For a :class:`GraphConvModel <deepchem.models.GraphConvModel>`, we'll reload our datasets with :code:`featurizer='GraphConv'`:

.. doctest:: sampl

.. doctest:: delaney

    >>> seed_all()

    >>> # Load SAMPL dataset

    >>> SAMPL_tasks, SAMPL_datasets, transformers = dc.molnet.load_sampl(

    ...     featurizer='GraphConv')

    >>> train_dataset, valid_dataset, test_dataset = SAMPL_datasets

    >>> tasks, datasets, transformers = dc.molnet.load_delaney(featurizer='GraphConv')

    >>> train_dataset, valid_dataset, test_dataset = datasets

    >>>

    >>> model = dc.models.GraphConvModel(len(SAMPL_tasks), mode='regression')

    >>> model = dc.models.GraphConvModel(len(tasks), mode='regression', dropout=0.5)

    >>>

    >>> model.fit(train_dataset, nb_epoch=20)

    >>> model.fit(train_dataset, nb_epoch=30)

    0...

    >>>

    >>> # We now evaluate our fitted model on our training and validation sets