:rewind: revert changes (31efa85f) · Commits · 钟慕尧 / deepchem

deepchem/hyper/grid_search.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -155,7 +155,7 @@ 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, multitask_scores)
		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
		@@ -182,7 +182,7 @@ class GridHyperparamOpt(HyperparamOpt):
		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, multitask_scores)
		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)

+3 −3

Original line number	Diff line number	Diff line
		import logging
		from typing import Callable, Optional
		from typing import Any, Callable, Optional

		import numpy as np

		@@ -443,8 +443,8 @@ class Metric(object):
		"""

		def __init__(self,
		metric: Callable,
		task_averager: Optional[Callable] = None,
		metric: Callable[..., float],
		task_averager: Optional[Callable[..., Any]] = None,
		name: Optional[str] = None,
		threshold: Optional[float] = None,
		mode: Optional[str] = None,

+7 −5

Original line number	Diff line number	Diff line
		@@ -11,10 +11,12 @@ from deepchem.metrics import Metric

		logger = logging.getLogger(__name__)

		Metrics = Union[Metric, Callable, List[Metric], List[Callable]]
		Score = Dict[str, float]
		Metric_Func = Callable[..., Any]
		Metrics = Union[Metric, Metric_Func, List[Metric], List[Metric_Func]]


		def output_statistics(scores: Dict, stats_out: str):
		def output_statistics(scores: Score, stats_out: str) -> None:
		"""Write computed stats to file.

		Statistics are written to specified `stats_out` file.
		@@ -192,7 +194,7 @@ class Evaluator(object):
		transformer for transformer in transformers if transformer.transform_y
		]

		def output_statistics(self, scores: Dict, stats_out: str):
		def output_statistics(self, scores: Score, stats_out: str):
		""" Write computed stats to file.

		Parameters
		@@ -243,7 +245,7 @@ class Evaluator(object):
		stats_out: Optional[str] = None,
		per_task_metrics: bool = False,
		use_sample_weights: bool = False,
		n_classes: int = 2) -> Union[Dict, Tuple[Dict, Dict]]:
		n_classes: int = 2) -> Union[Score, Tuple[Score, Score]]:
		"""
		Computes statistics of model on test data and saves results to csv.

		@@ -397,7 +399,7 @@ class GeneratorEvaluator(object):
		metrics: Metrics,
		per_task_metrics: bool = False,
		use_sample_weights: bool = False,
		n_classes: int = 2) -> Union[Dict, Tuple[Dict, Dict]]:
		n_classes: int = 2) -> Union[Score, Tuple[Score, Score]]:
		"""
		Computes statistics of model on test data and saves results to csv.