use > assertions and ellipsis wildcards

        np.random.seed(123)

        tf.random.set_seed(123)

Other notes:

* We sometimes match against doctest's ellipsis wild card  on code that where output is usually ignored (e.g. :code:`0...` for :code:`model.fit`)

* We often use heuristic assertions (e.g. :code:`score['mean-pearson_r2_score'] > 0.92`) as deterministic output is brittle and less important in model training code.

SAMPL (FreeSolv)

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

    ...     learning_rate=0.001,

    ...     batch_size=50)

    >>>

    >>> # Fit trained model (returns average loss over the most recent checkpoint interval)

    >>> model.fit(train_dataset)

    0.1726440668106079

    0...

    >>>

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

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

    {'mean-pearson_r2_score': 0.9244964295814636}

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

    {'mean-pearson_r2_score': 0.7532658569385681}

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

    >>> assert train_scores['mean-pearson_r2_score'] > 0.92

    >>>

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

    >>> assert valid_scores['mean-pearson_r2_score'] > 0.75

GraphConvModel

    >>>

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

    >>> 

    >>> # Fit trained model (returns average loss over the most recent checkpoint interval)

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

    0.05753047466278076

    0...

    >>> 

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

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

    {'mean-pearson_r2_score': 0.5772751202910659}

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

    {'mean-pearson_r2_score': 0.36771456280565507}

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

    >>> assert train_scores['mean-pearson_r2_score'] > 0.57

    >>>

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

    >>> assert valid_scores['mean-pearson_r2_score'] > 0.36

..

      ...     batch_size=100,

      ...     verbosity="high")

      >>>

      >>> model.fit(train_dataset, nb_epoch=10) # orig. 20

      0.04922508895397186

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

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

      {'mean-pearson_r2_score': nan}

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

      {'mean-pearson_r2_score': nan}

      >>> model.evaluate(test_dataset, [avg_pearson_r2], transformers)

      {'mean-pearson_r2_score': nan}

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

      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'] > 0.00 # is currently nan

      >>>

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

      >>> assert valid_scores['mean-pearson_r2_score'] > 0.00 # is currently nan

  GraphConvModel

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

      >>>

      >>> # Fit trained model

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

      None

      >>>

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

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

      {'mean-pearson_r2_score': nan}

      >>> model.evaluate(valid_dataset, [avg_pearson_r2], transformers) and False

      {'mean-pearson_r2_score': nan}

      >>> model.evaluate(test_dataset, [avg_pearson_r2], transformers) and False

      {'mean-pearson_r2_score': nan}

      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'] > 0.00 # is currently nan

      >>>

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

      >>> assert valid_scores['mean-pearson_r2_score'] > 0.00 # is currently nan