Merge pull request #2223 from deepchem/chembl

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

..

ChEMBL

-------

    >>> f'Compound train/valid/test split: {len(train_dataset)}/{len(valid_dataset)}/{len(test_dataset)}'

    'Compound train/valid/test split: 19096/2387/2388'

    >>>

      >>> # 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 want to know the RMS, averaged across tasks

    >>> avg_rms = dc.metrics.Metric(dc.metrics.rms_score, np.mean)

    >>>

    >>> # Create our model

    >>> n_layers = 3

    >>> model = dc.models.MultitaskRegressor(

    ...     len(chembl_tasks),

      ...     train_dataset.get_data_shape()[0],

    ...     n_features=1024,

    ...     layer_sizes=[1000] * n_layers,

    ...     dropouts=[.25] * n_layers,

    ...     weight_init_stddevs=[.02] * n_layers,

    ...     bias_init_consts=[1.] * n_layers,

    ...     learning_rate=.0003,

    ...     weight_decay_penalty=.0001,

      ...     batch_size=100,

      ...     verbosity="high")

    ...     batch_size=100)

    >>>

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

    >>> model.fit(train_dataset, nb_epoch=5)

    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

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

    >>> assert train_scores['mean-rms_score'] < 10.00 

    >>>

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

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

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

    >>> assert valid_scores['mean-rms_score'] < 10.00 

GraphConvModel

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

    ...    shard_size=2000, featurizer="GraphConv", set="5thresh", split="random")

    >>> train_dataset, valid_dataset, test_dataset = datasets

    >>> 

      >>> # pearson R squared score, averaged across tasks

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

    >>> # RMS, averaged across tasks

    >>> avg_rms = dc.metrics.Metric(dc.metrics.rms_score, np.mean)

    >>>

    >>> model = dc.models.GraphConvModel(

    ...    len(chembl_tasks), batch_size=128, mode='regression')

    >>>

    >>> # Fit trained model

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

    >>> model.fit(train_dataset, nb_epoch=5)

    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

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

    >>> assert train_scores['mean-rms_score'] < 10.00 

    >>>

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

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

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

    >>> assert valid_scores['mean-rms_score'] < 10.00