:sparkles: fix type and docs (d8be13f1) · Commits · 钟慕尧 / deepchem

deepchem/feat/graph_data.py

+1 −1

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		@@ -119,7 +119,7 @@ class GraphData:
		Returns
		-------
		dgl.DGLGraph
		Graph data for PyTorch Geometric
		Graph data for DGL

		Notes
		-----

+5 −1

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		@@ -10,7 +10,7 @@ import collections
		import logging
		from functools import reduce
		from operator import mul
		from typing import Dict, List, Optional
		from typing import cast, Dict, List, Optional

		from deepchem.data import Dataset
		from deepchem.trans import Transformer
		@@ -155,6 +155,8 @@ class GridHyperparamOpt(HyperparamOpt):

		evaluator = Evaluator(model, valid_dataset, output_transformers)
		multitask_scores = evaluator.compute_model_performance([metric])
		# NOTE: this casting is workaround. This line doesn't effect anything to the runtime
		multitask_scores = cast(Dict[str, float], multitask_scores)
		valid_score = multitask_scores[metric.name]
		hp_str = _convert_hyperparam_dict_to_filename(hyper_params)
		all_scores[hp_str] = valid_score
		@@ -180,6 +182,8 @@ class GridHyperparamOpt(HyperparamOpt):
		return best_model, best_hyperparams, all_scores
		train_evaluator = Evaluator(best_model, train_dataset, output_transformers)
		multitask_scores = train_evaluator.compute_model_performance([metric])
		# NOTE: this casting is workaround. This line doesn't effect anything to the runtime
		multitask_scores = cast(Dict[str, float], multitask_scores)
		train_score = multitask_scores[metric.name]
		logger.info("Best hyperparameters: %s" % str(best_hyperparams))
		logger.info("train_score: %f" % train_score)

+2 −2

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		@@ -484,8 +484,8 @@ def cosine_dist(x, y):
		themselves could be different batches. Using vectors or tensors of all 0s
		should be avoided.

		Method
		------
		Methods
		-------
		The vectors in the input tensors are first l2-normalized such that each vector
		has length or magnitude of 1. The inner product (dot product) is then taken
		between corresponding pairs of row vectors in the input tensors and returned.

+2 −2

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		@@ -41,8 +41,8 @@ class Model(BaseEstimator):
		This is intended only for convenience of subclass implementations
		and should not be invoked directly.

		Parameters:
		-----------
		Parameters
		----------
		model_instance: object
		Wrapper around ScikitLearn/Keras/Tensorflow model object.
		model_dir: str, optional (default None)

+7 −7

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		@@ -47,7 +47,7 @@ class Optimizer(object):
		-------
		a new PyTorch optimizer implementing the algorithm
		"""
		raise NotImplemented("Subclasses must implement this")
		raise NotImplementedError("Subclasses must implement this")


		class LearningRateSchedule(object):
		@@ -82,7 +82,7 @@ class LearningRateSchedule(object):
		-------
		a PyTorch scheduler implementing the schedule
		"""
		raise NotImplemented("Subclasses must implement this")
		raise NotImplementedError("Subclasses must implement this")


		class AdaGrad(Optimizer):
		@@ -93,8 +93,8 @@ class AdaGrad(Optimizer):
		The more updates a parameter receives, the smaller the updates. See [1]_ for
		a full reference for the algorithm.

		Returns
		-------
		References
		----------
		.. [1] Duchi, John, Elad Hazan, and Yoram Singer. "Adaptive subgradient
		methods for online learning and stochastic optimization." Journal of machine
		learning research 12.7 (2011).