Merge branch 'master' into sklearn-model (9829acd6) · Commits · 钟慕尧 / deepchem

README.md

+1 −1

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		@@ -73,7 +73,7 @@ conda install -y -c conda-forge rdkit deepchem==2.3.0
		You install the nightly build version via pip. The nightly version is built by the HEAD of DeepChem.

		```bash
		pip install tensorflow==2.2.0
		pip install tensorflow==2.3.0
		pip install --pre deepchem
		```

+12 −11

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		@@ -134,6 +134,7 @@ class Featurizer(object):

		override_args_info = ''
		for arg_name, default in zip(args_names, args_default_values):
		if arg_name in self.__dict__:
		arg_value = self.__dict__[arg_name]
		# validation
		# skip list

+2 −2

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		@@ -344,8 +344,8 @@ class MultitaskRegressor(KerasModel):
		class MultitaskFitTransformRegressor(MultitaskRegressor):
		"""Implements a MultitaskRegressor that performs on-the-fly transformation during fit/predict.

		Example:

		Examples
		--------
		>>> n_samples = 10
		>>> n_features = 3
		>>> n_tasks = 1

+6 −4

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		@@ -559,7 +559,9 @@ class KerasModel(Model):
		returns the values of the uncertainty outputs.
		other_output_types: list, optional
		Provides a list of other output_types (strings) to predict from model.
		Returns:

		Returns
		-------
		a NumPy array of the model produces a single output, or a list of arrays
		if it produces multiple outputs
		"""
		@@ -784,7 +786,7 @@ class KerasModel(Model):
		if it produces multiple outputs
		"""
		generator = self.default_generator(
		dataset, mode='predict', pad_batches=False)
		dataset, mode='predict', deterministic=True, pad_batches=False)
		return self.predict_on_generator(
		generator,
		transformers=transformers,

+19 −2

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		@@ -3,10 +3,12 @@ import tensorflow as tf
		import collections

		import logging
		import deepchem as dc
		from deepchem.metrics import to_one_hot
		from deepchem.models import KerasModel
		from deepchem.models.layers import Stack
		from deepchem.models.losses import SoftmaxCrossEntropy, L2Loss
		from typing import Tuple, Iterable, List

		logger = logging.getLogger(__name__)

		@@ -348,6 +350,21 @@ class RobustMultitaskRegressor(KerasModel):
		task_out = tf.keras.layers.Dense(1)(task_layer)
		task_outputs.append(task_out)

		outputs = tf.keras.layers.Concatenate(axis=1)(task_outputs)
		outputs = Stack(axis=1)(task_outputs)
		model = tf.keras.Model(inputs=mol_features, outputs=outputs)
		super(RobustMultitaskRegressor, self).__init__(model, L2Loss(), **kwargs)
		super(RobustMultitaskRegressor, self).__init__(
		model, L2Loss(), output_types=['prediction'], **kwargs)

		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,
		deterministic=deterministic,
		pad_batches=pad_batches):
		yield ([X_b], [y_b], [w_b])