:rotating_light: apply flake8 in rl and metaleanring (8f1d6a7c) · Commits · 钟慕尧 / deepchem

deepchem/metalearning/init.py

+2 −0

Original line number	Diff line number	Diff line
		# flake8: noqa

		from deepchem.metalearning.maml import MAML, MetaLearner

deepchem/metalearning/maml.py

+8 −7

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		"""Model-Agnostic Meta-Learning (MAML) algorithm for low data learning."""

		from deepchem.models.optimizers import Adam, GradientDescent
		import numpy as np
		import os
		import shutil
		import tempfile
		import tensorflow as tf
		import time

		import tensorflow as tf

		from deepchem.models.optimizers import Adam, GradientDescent


		class MetaLearner(object):
		"""Model and data to which the MAML algorithm can be applied.
		@@ -37,12 +38,12 @@ class MetaLearner(object):
		(loss, outputs) where loss is the value of the model's loss function, and
		outputs is a list of the model's outputs
		"""
		raise NotImplemented("Subclasses must implement this")
		raise NotImplementedError("Subclasses must implement this")

		@property
		def variables(self):
		"""Get the list of Tensorflow variables to train."""
		raise NotImplemented("Subclasses must implement this")
		raise NotImplementedError("Subclasses must implement this")

		def select_task(self):
		"""Select a new task to train on.
		@@ -51,7 +52,7 @@ class MetaLearner(object):
		If there are infinitely many training tasks, this can simply select a new one each
		time it is called.
		"""
		raise NotImplemented("Subclasses must implement this")
		raise NotImplementedError("Subclasses must implement this")

		def get_batch(self):
		"""Get a batch of data for training.
		@@ -60,7 +61,7 @@ class MetaLearner(object):
		inputs. This will usually be called twice for each task, and should
		return a different batch on each call.
		"""
		raise NotImplemented("Subclasses must implement this")
		raise NotImplementedError("Subclasses must implement this")


		class MAML(object):

deepchem/metalearning/tests/test_maml.py

+4 −3

Original line number	Diff line number	Diff line
		from flaky import flaky
		import unittest

		import deepchem as dc
		import numpy as np
		import tensorflow as tf
		import unittest
		from flaky import flaky

		import deepchem as dc


		class TestMAML(unittest.TestCase):

deepchem/rl/init.py

+6 −5

Original line number	Diff line number	Diff line
		"""Interface for reinforcement learning."""

		from deepchem.rl.a2c import A2C
		from deepchem.rl.ppo import PPO

		from deepchem.rl.a2c import A2C # noqa: F401
		from deepchem.rl.ppo import PPO # noqa: F401


		class Environment(object):
		@@ -120,7 +121,7 @@ class Environment(object):
		This must be called before calling step() or querying the state. You can call it
		again later to reset the environment back to its original state.
		"""
		raise NotImplemented("Subclasses must implement this")
		raise NotImplementedError("Subclasses must implement this")

		def step(self, action):
		"""Take a time step by performing an action.
		@@ -137,7 +138,7 @@ class Environment(object):
		the reward earned by taking the action, represented as a floating point number
		(higher values are better)
		"""
		raise NotImplemented("Subclasses must implement this")
		raise NotImplementedError("Subclasses must implement this")


		class GymEnvironment(Environment):
		@@ -225,4 +226,4 @@ class Policy(object):
		Depending on the algorithm being used, other inputs might get passed as
		well. It is up to each algorithm to document that.
		"""
		raise NotImplemented("Subclasses must implement this")
		raise NotImplementedError("Subclasses must implement this")

deepchem/rl/a2c.py

+6 −11

Original line number	Diff line number	Diff line
		"""Advantage Actor-Critic (A2C) algorithm for reinforcement learning."""
		import time
		import collections

		from deepchem.models import KerasModel
		from deepchem.models.optimizers import Adam
		import numpy as np
		import tensorflow as tf
		import collections
		import copy
		import multiprocessing
		import os
		import re
		import threading
		import time

		from deepchem.models import KerasModel
		from deepchem.models.optimizers import Adam


		class A2CLossDiscrete(object):
		@@ -49,7 +45,7 @@ class A2CLossContinuous(object):
		def __init__(self, value_weight, entropy_weight, mean_index, std_index,
		value_index):
		try:
		import tensorflow_probability as tfp
		import tensorflow_probability as tfp # noqa: F401
		except ModuleNotFoundError:
		raise ValueError(
		"This class requires tensorflow-probability to be installed.")
		@@ -384,7 +380,6 @@ class A2C(object):

		def _create_rollout(self, rnn_states):
		"""Generate a rollout."""
		n_actions = self._env.n_actions
		states = []
		actions = []
		rewards = []

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