sampl model examples to docs/examples.rst

datasets/pdbbind_v2019_other_PL.tar.gz

datasets/pdbbind_v2019_refined.tar.gz

datasets/qm8.csv

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Examples

========

SAMPL models

============

Some examples of training models on the SAMPL(FreeSolv) dataset included in :code:`dc.molnet`.

First we'll load our tasks, dataset  splits, and transformers from molnet:

.. doctest::

   >>> import numpy as np

   >>> import tensorflow as tf

   >>> import deepchem as dc

   >>> from deepchem.molnet import load_sampl

   >>> 

   >>> # for reproducibility 

   >>> np.random.seed(123)

   >>> tf.random.set_seed(123)

.. doctest:: 

   >>> # Load SAMPL dataset

   >>> SAMPL_tasks, SAMPL_datasets, transformers = load_sampl()

   >>> train_dataset, valid_dataset, test_dataset = SAMPL_datasets

   >>>

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

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

   >>> 

   >>> model = dc.models.MultitaskRegressor(

   ...     len(SAMPL_tasks),

   ...     n_features = 1024,

   ...     layer_sizes=[1000],

   ...     dropouts=[.25],

   ...     learning_rate=0.001,

   ...     batch_size=50)

   >>> 

   >>> # Fit trained model

   >>> model.fit(train_dataset)

   0.1726440668106079

   >>> 

   >>> # We now evaluate our fitted model on our train and test sets

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

   {'mean-pearson_r2_score': 0.9244964295814636}

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

   {'mean-pearson_r2_score': 0.7532658569385681}

For a :code:`GraphConvModel` we'll need to reload with the appropriate featurizer:

.. doctest:: 

   >>> # for reproducibility 

   >>> np.random.seed(123)

   >>> tf.random.set_seed(123)

   >>> # Load SAMPL dataset

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

   ...     featurizer='GraphConv')

   >>> train_dataset, valid_dataset, test_dataset = SAMPL_datasets

   >>>

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

   >>> 

   >>> # Fit trained model

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

   0.05753047466278076

   >>> 

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

   {'mean-pearson_r2_score': 0.5772751202910659}

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

   {'mean-pearson_r2_score': 0.36771456280565507}

   :caption: Get Started

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