Merge pull request #1979 from peastman/types (a5091674) · Commits · 钟慕尧 / deepchem

.travis.yml

+2 −0

Original line number	Diff line number	Diff line
		@@ -26,12 +26,14 @@ install:
		- bash scripts/install_deepchem_conda.sh deepchem
		- conda activate deepchem
		- python setup.py install
		- conda install mypy
		- pip install coveralls yapf==0.22.0
		script:
		- pytest -m "not slow" --cov=deepchem deepchem
		- if [ $TRAVIS_PYTHON_VERSION == '3.7' ]; then
		find ./deepchem -name ".py" ! -name 'load_dataset_template.py' \| xargs python -m doctest -v; fi
		- bash devtools/travis-ci/test_format_code.sh
		- mypy -p deepchem --ignore-missing-imports
		- if [ $TRAVIS_PYTHON_VERSION == '3.7' ]; then
		cd docs && pip install -r requirements.txt && make clean html && cd ..; fi
		after_success:

deepchem/data/datasets.py

+193 −152

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deepchem/models/fcnet.py

+64 −54

Original line number	Diff line number	Diff line
		@@ -15,6 +15,9 @@ from deepchem.utils.save import log
		from deepchem.metrics import to_one_hot
		from tensorflow.keras.layers import Input, Dense, Reshape, Softmax, Dropout, Activation, Lambda

		from typing import Any, Callable, Iterable, List, Optional, Sequence, Tuple, Union
		from deepchem.utils.typing import KerasActivationFn, KerasLossFn, OneOrMany

		logger = logging.getLogger(__name__)


		@@ -34,18 +37,18 @@ class MultitaskClassifier(KerasModel):
		"""

		def __init__(self,
		n_tasks,
		n_features,
		layer_sizes=[1000],
		weight_init_stddevs=0.02,
		bias_init_consts=1.0,
		weight_decay_penalty=0.0,
		weight_decay_penalty_type="l2",
		dropouts=0.5,
		activation_fns=tf.nn.relu,
		n_classes=2,
		residual=False,
		**kwargs):
		n_tasks: int,
		n_features: int,
		layer_sizes: Sequence[int] = [1000],
		weight_init_stddevs: OneOrMany[float] = 0.02,
		bias_init_consts: OneOrMany[float] = 1.0,
		weight_decay_penalty: float = 0.0,
		weight_decay_penalty_type: str = "l2",
		dropouts: OneOrMany[float] = 0.5,
		activation_fns: OneOrMany[KerasActivationFn] = tf.nn.relu,
		n_classes: int = 2,
		residual: bool = False,
		**kwargs) -> None:
		"""Create a MultitaskClassifier.

		In addition to the following arguments, this class also accepts
		@@ -66,7 +69,7 @@ class MultitaskClassifier(KerasModel):
		equal len(layer_sizes). Alternatively this may be a single
		value instead of a list, in which case the same value is used
		for every layer.
		bias_init_consts: list or loat
		bias_init_consts: list or float
		the value to initialize the biases in each layer to. The
		length of this list should equal len(layer_sizes).
		Alternatively this may be a single value instead of a list, in
		@@ -150,12 +153,13 @@ class MultitaskClassifier(KerasModel):
		output_types=['prediction', 'loss'],
		**kwargs)

		def default_generator(self,
		dataset,
		epochs=1,
		mode='fit',
		deterministic=True,
		pad_batches=True):
		def default_generator(
		self,
		dataset: dc.data.Dataset,
		epochs: int = 1,
		mode: str = 'fit',
		deterministic: bool = True,
		pad_batches: bool = True) -> Iterable[Tuple[List, List, List]]:
		for epoch in range(epochs):
		for (X_b, y_b, w_b, ids_b) in dataset.iterbatches(
		batch_size=self.batch_size,
		@@ -183,18 +187,18 @@ class MultitaskRegressor(KerasModel):
		"""

		def __init__(self,
		n_tasks,
		n_features,
		layer_sizes=[1000],
		weight_init_stddevs=0.02,
		bias_init_consts=1.0,
		weight_decay_penalty=0.0,
		weight_decay_penalty_type="l2",
		dropouts=0.5,
		activation_fns=tf.nn.relu,
		uncertainty=False,
		residual=False,
		**kwargs):
		n_tasks: int,
		n_features: int,
		layer_sizes: Sequence[int] = [1000],
		weight_init_stddevs: OneOrMany[float] = 0.02,
		bias_init_consts: OneOrMany[float] = 1.0,
		weight_decay_penalty: float = 0.0,
		weight_decay_penalty_type: str = "l2",
		dropouts: OneOrMany[float] = 0.5,
		activation_fns: OneOrMany[KerasActivationFn] = tf.nn.relu,
		uncertainty: bool = False,
		residual: bool = False,
		**kwargs) -> None:
		"""Create a MultitaskRegressor.

		In addition to the following arguments, this class also accepts all the keywork arguments
		@@ -296,6 +300,7 @@ class MultitaskRegressor(KerasModel):
		stddev=weight_init_stddevs[-1]),
		bias_initializer=tf.constant_initializer(
		value=bias_init_consts[-1]))(prev_layer))
		loss: Union[dc.models.losses.Loss, KerasLossFn]
		if uncertainty:
		log_var = Reshape((n_tasks, 1))(Dense(
		n_tasks,
		@@ -318,12 +323,13 @@ class MultitaskRegressor(KerasModel):
		super(MultitaskRegressor, self).__init__(
		model, loss, output_types=output_types, **kwargs)

		def default_generator(self,
		dataset,
		epochs=1,
		mode='fit',
		deterministic=True,
		pad_batches=True):
		def default_generator(
		self,
		dataset: dc.data.Dataset,
		epochs: int = 1,
		mode: str = 'fit',
		deterministic: bool = True,
		pad_batches: bool = True) -> Iterable[Tuple[List, List, List]]:
		for epoch in range(epochs):
		for (X_b, y_b, w_b, ids_b) in dataset.iterbatches(
		batch_size=self.batch_size,
		@@ -358,10 +364,10 @@ class MultitaskFitTransformRegressor(MultitaskRegressor):
		"""

		def __init__(self,
		n_tasks,
		n_features,
		fit_transformers=[],
		batch_size=50,
		n_tasks: int,
		n_features: int,
		fit_transformers: Sequence[dc.trans.Transformer] = [],
		batch_size: int = 50,
		**kwargs):
		"""Create a MultitaskFitTransformRegressor.

		@@ -388,18 +394,21 @@ class MultitaskFitTransformRegressor(MultitaskRegressor):
		else:
		raise ValueError("n_features should be list or int")
		for transformer in fit_transformers:
		X_b = transformer.X_transform(X_b)
		assert transformer.transform_X and not (transformer.transform_y or
		transformer.transform_w)
		X_b, _, _ = transformer.transform_array(X_b, None, None)
		n_features = X_b.shape[1]
		logger.info("n_features after fit_transform: %d", int(n_features))
		super(MultitaskFitTransformRegressor, self).__init__(
		n_tasks, n_features, batch_size=batch_size, **kwargs)

		def default_generator(self,
		dataset,
		epochs=1,
		mode='fit',
		deterministic=True,
		pad_batches=True):
		def default_generator(
		self,
		dataset: dc.data.Dataset,
		epochs: int = 1,
		mode: str = 'fit',
		deterministic: bool = True,
		pad_batches: bool = True) -> Iterable[Tuple[List, List, List]]:
		for epoch in range(epochs):
		for (X_b, y_b, w_b, ids_b) in dataset.iterbatches(
		batch_size=self.batch_size,
		@@ -410,18 +419,19 @@ class MultitaskFitTransformRegressor(MultitaskRegressor):
		if X_b is not None:
		if mode == 'fit':
		for transformer in self.fit_transformers:
		X_b = transformer.X_transform(X_b)
		X_b, _, _ = transformer.transform_array(X_b, None, None)
		if mode == 'predict':
		dropout = np.array(0.0)
		else:
		dropout = np.array(1.0)
		yield ([X_b, dropout], [y_b], [w_b])

		def predict_on_generator(self,
		generator,
		transformers=[],
		outputs=None,
		output_types=None):
		def predict_on_generator(
		self,
		generator: Iterable[Tuple[Any, Any, Any]],
		transformers: List[dc.trans.Transformer] = [],
		outputs: Optional[OneOrMany[tf.Tensor]] = None,
		output_types: Optional[OneOrMany[str]] = None) -> OneOrMany[np.ndarray]:

		def transform_generator():
		for inputs, labels, weights in generator:

deepchem/models/keras_model.py

+146 −106

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deepchem/models/models.py

+66 −21

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