update docs (fedf0b85) · Commits · 钟慕尧 / deepchem

deepchem/hyper/gaussian_process.py

+67 −15

Original line number	Diff line number	Diff line
		"""
		Contains basic hyperparameter optimizations.
		Contains class for gaussian process hyperparameter optimizations.
		"""
		from __future__ import print_function
		from __future__ import division
		from __future__ import unicode_literals

		import numpy as np
		import tempfile
		import deepchem
		from deepchem.utils.evaluate import Evaluator
		from deepchem.hyper import HyperparamOpt
		from deepchem.molnet.run_benchmark_models import benchmark_classification, benchmark_regression
		from deepchem.utils.dependencies import pyGPGO_covfunc, pyGPGO_acquisition, \
		pyGPGO_surrogates_GaussianProcess, pyGPGO_GPGO


		class GaussianProcessHyperparamOpt(HyperparamOpt):
		class GaussianProcessHyperparamOpt(deepchem.hyper.HyperparamOpt):
		"""
		Gaussian Process Global Optimization(GPGO)
		"""
		@@ -39,6 +43,39 @@ class GaussianProcessHyperparamOpt(HyperparamOpt):

		For Molnet models, self.model_class is model name in string,
		params_dict = dc.molnet.preset_hyper_parameters.hps[self.model_class]

		Parameters
		----------
		params_dict: dict
		dict including parameters and their initial values
		parameters not suitable for optimization can be added to hp_invalid_list
		train_dataset: dc.data.Dataset struct
		dataset used for training
		valid_dataset: dc.data.Dataset struct
		dataset used for validation(optimization on valid scores)
		output_transformers: list of dc.trans.Transformer
		transformers for evaluation
		metric: list of dc.metrics.Metric
		metric used for evaluation
		n_features: int
		number of input features
		n_tasks: int
		number of tasks
		max_iter: int
		number of optimization trials
		search_range: int(float)
		optimization on [initial values / search_range,
		initial values * search_range]
		hp_invalid_list: list
		names of parameters that should not be optimized

		Returns
		-------
		hyper_parameters: dict
		params_dict with all optimized values
		valid_performance_opt: float
		best performance on valid dataset

		"""

		assert len(metric) == 1, 'Only use one metric'
		@@ -51,21 +88,22 @@ class GaussianProcessHyperparamOpt(HyperparamOpt):
		hp_list_class = [hyper_parameters[hp].__class__ for hp in hp_list]
		assert set(hp_list_class) <= set([list, int, float])
		# Float or int hyper parameters(ex. batch_size, learning_rate)
		hp_list1 = [
		hp_list_single = [
		hp_list[i] for i in range(len(hp_list)) if not hp_list_class[i] is list
		]
		# List of float or int hyper parameters(ex. layer_sizes)
		hp_list2 = [(hp_list[i], len(hyper_parameters[hp_list[i]]))
		for i in range(len(hp_list)) if hp_list_class[i] is list]
		hp_list_multiple = [(hp_list[i], len(hyper_parameters[hp_list[i]]))
		for i in range(len(hp_list))
		if hp_list_class[i] is list]

		# Number of parameters
		n_param = len(hp_list1) + sum([hp[1] for hp in hp_list2])
		n_param = len(hp_list_single + sum([hp[1] for hp in hp_list_multiple]))
		# Range of optimization
		param_range = []
		for hp in hp_list1:
		for hp in hp_list_single:
		if hyper_parameters[hp].__class__ is int:
		param_range.append((('int'), [
		hyper_parameters[hp] / search_range,
		hyper_parameters[hp] // search_range,
		hyper_parameters[hp] * search_range
		]))
		else:
		@@ -73,10 +111,10 @@ class GaussianProcessHyperparamOpt(HyperparamOpt):
		hyper_parameters[hp] / search_range,
		hyper_parameters[hp] * search_range
		]))
		for hp in hp_list2:
		for hp in hp_list_multiple:
		if hyper_parameters[hp[0]][0].__class__ is int:
		param_range.extend([(('int'), [
		hyper_parameters[hp[0]][i] / search_range,
		hyper_parameters[hp[0]][i] // search_range,
		hyper_parameters[hp[0]][i] * search_range
		]) for i in range(hp[1])])
		else:
		@@ -109,15 +147,29 @@ class GaussianProcessHyperparamOpt(HyperparamOpt):
		l17=0,
		l18=0,
		l19=0):
		""" Optimizing function
		Take in hyper parameter values and return valid set performances

		Parameters
		----------
		l00~l19: int or float
		placeholders for hyperparameters being optimized,
		hyper_parameters dict is rebuilt based on input values of placeholders

		Returns:
		--------
		valid_scores: float
		valid set performances
		"""
		args = locals()
		# Input hyper parameters
		i = 0
		for hp in hp_list1:
		for hp in hp_list_single:
		hyper_parameters[hp] = float(args[param_name[i]])
		if param_range[i][0] == 'int':
		hyper_parameters[hp] = int(hyper_parameters[hp])
		i = i + 1
		for hp in hp_list2:
		for hp in hp_list_multiple:
		hyper_parameters[hp[0]] = [
		float(args[param_name[j]]) for j in range(i, i + hp[1])
		]
		@@ -169,12 +221,12 @@ class GaussianProcessHyperparamOpt(HyperparamOpt):

		# Readout best hyper parameters
		i = 0
		for hp in hp_list1:
		for hp in hp_list_single:
		hyper_parameters[hp] = float(hp_opt[param_name[i]])
		if param_range[i][0] == 'int':
		hyper_parameters[hp] = int(hyper_parameters[hp])
		i = i + 1
		for hp in hp_list2:
		for hp in hp_list_multiple:
		hyper_parameters[hp[0]] = [
		float(hp_opt[param_name[j]]) for j in range(i, i + hp[1])
		]

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