Loading deepchem/hyper/GP_hyperparameter_optimization.py 0 → 100644 +153 −0 Original line number Diff line number Diff line #!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Fri Jul 28 17:30:59 2017 @author: zqwu """ from __future__ import print_function from __future__ import division from __future__ import unicode_literals import numpy as np import deepchem from deepchem.molnet.preset_hyper_parameters import hps import csv import copy from deepchem.utils.dependencies import pyGPGO_covfunc, pyGPGO_acquisition, \ pyGPGO_surrogates_GaussianProcess, pyGPGO_GPGO def GaussianProcessHyperparamOpt(dataset='tox21', model='tf', split='random', max_iter=20, search_range=4, hp_invalid_list=[ 'seed', 'nb_epoch', 'penalty_type', 'dropouts', 'bypass_dropouts', 'n_pair_feat' ]): hyper_parameters = copy.deepcopy(hps[model]) # Extract parameters hp_list = hyper_parameters.keys() for hp in hp_invalid_list: if hp in hp_list: hp_list.remove(hp) 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[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] # Number of parameters n_param = len(hp_list1) + sum([hp[1] for hp in hp_list2]) # Range of optimization param_range = [] for hp in hp_list1: if hyper_parameters[hp].__class__ is int: param_range.append((('int'), [ hyper_parameters[hp] / search_range, hyper_parameters[hp] * search_range ])) else: param_range.append((('cont'), [ hyper_parameters[hp] / search_range, hyper_parameters[hp] * search_range ])) for hp in hp_list2: 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 ]) for i in range(hp[1])]) else: param_range.extend([(('cont'), [ hyper_parameters[hp[0]][i] / search_range, hyper_parameters[hp[0]][i] * search_range ]) for i in range(hp[1])]) # Dummy names param_name = ['l' + format(i, '02d') for i in range(20)] param = dict(zip(param_name[:n_param], param_range)) def f(l00=0, l01=0, l02=0, l03=0, l04=0, l05=0, l06=0, l07=0, l08=0, l09=0, l10=0, l11=0, l12=0, l13=0, l14=0, l15=0, l16=0, l17=0, l18=0, l19=0): args = locals() # Input hyper parameters i = 0 for hp in hp_list1: 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: hyper_parameters[hp[0]] = [ float(args[param_name[j]]) for j in range(i, i + hp[1]) ] if param_range[i][0] == 'int': hyper_parameters[hp[0]] = map(int, hyper_parameters[hp[0]]) i = i + hp[1] print(hyper_parameters) # Run benchmark deepchem.molnet.run_benchmark( [dataset], str(model), split=str(split), hyper_parameters=hyper_parameters, out_path='/tmp') # Read results with open('/tmp/results.csv', 'r') as f: reader = csv.reader(f) # Return valid set performances return float(list(reader)[-1][8]) cov = pyGPGO_covfunc.matern32() gp = pyGPGO_surrogates_GaussianProcess.GaussianProcess(cov) acq = pyGPGO_acquisition.Acquisition(mode='ExpectedImprovement') gpgo = pyGPGO_GPGO.GPGO(gp, acq, f, param) gpgo.run(max_iter=max_iter) hp_opt, valid_performance_opt = gpgo.getResult() # Readout best hyper parameters i = 0 for hp in hp_list1: 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: hyper_parameters[hp[0]] = [ float(hp_opt[param_name[j]]) for j in range(i, i + hp[1]) ] if param_range[i][0] == 'int': hyper_parameters[hp[0]] = map(int, hyper_parameters[hp[0]]) i = i + hp[1] return hyper_parameters, valid_performance_opt deepchem/hyper/__init__.py +1 −0 Original line number Diff line number Diff line Loading @@ -11,6 +11,7 @@ from functools import reduce from operator import mul from deepchem.utils.evaluate import Evaluator from deepchem.utils.save import log from deepchem.hyper.GP_hyperparameter_optimization import GaussianProcessHyperparamOpt class HyperparamOpt(object): Loading deepchem/utils/dependencies.py +5 −0 Original line number Diff line number Diff line Loading @@ -18,3 +18,8 @@ xgboost = import_lazy("xgboost") pdbfixer = import_lazy("pdbfixer") simtk = import_lazy("simtk") mdtraj = import_lazy("mdtraj") pyGPGO_covfunc = import_lazy("pyGPGO.covfunc") pyGPGO_acquisition = import_lazy("pyGPGO.acquisition") pyGPGO_surrogates_GaussianProcess = import_lazy( "pyGPGO.surrogates.GaussianProcess") pyGPGO_GPGO = import_lazy("pyGPGO.GPGO") examples/GPhyperparameterOptimization.pydeleted 100644 → 0 +0 −82 Original line number Diff line number Diff line #!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Fri Jul 28 17:30:59 2017 @author: zqwu """ from __future__ import print_function from __future__ import division from __future__ import unicode_literals import numpy as np np.random.seed(123) import tensorflow as tf tf.set_random_seed(123) import deepchem as dc from deepchem.molnet.preset_hyper_parameters import hps import csv from pyGPGO.covfunc import matern32 from pyGPGO.acquisition import Acquisition from pyGPGO.surrogates.GaussianProcess import GaussianProcess from pyGPGO.GPGO import GPGO model = 'graphconvreg' dataset = 'delaney' split = 'random' hyper_parameters = hps[model] # Extract parameters hp_list = hyper_parameters.keys() hp_list.remove('seed') hp_list.remove('nb_epoch') hp_list_class = [hyper_parameters[hp].__class__ for hp in hp_list] int_cont = [] for hp in hp_list_class: if hp is int: int_cont.append('int') else: int_cont.append('cont') # Range of optimization param_range = [(int_cont[i], [ hyper_parameters[hp_list[i]] / 4, hyper_parameters[hp_list[i]] * 4 ]) for i in range(len(int_cont))] # Number of parameters n_param = len(param_range) # Dummy names param_name = ['l' + str(i) for i in range(10)] param = dict(zip(param_name[:n_param], param_range)) def f(l0=0, l1=0, l2=0, l3=0, l4=0, l5=0, l6=0, l7=0, l8=0, l9=0): args = locals() keys = args.keys() keys.sort() # Input hyper parameters for i, hp in enumerate(hp_list): hyper_parameters[hp] = args[keys[i]] if int_cont[i] == 'int': hyper_parameters[hp] = int(hyper_parameters[hp]) print(hyper_parameters) # Run benchmark dc.molnet.run_benchmark( [dataset], str(model), split=str(split), hyper_parameters=hyper_parameters, out_path='/tmp') # Read results with open('/tmp/results.csv', 'r') as f: reader = csv.reader(f) return float(list(reader)[-1][8]) cov = matern32() gp = GaussianProcess(cov) acq = Acquisition(mode='ExpectedImprovement') gpgo = GPGO(gp, acq, f, param) gpgo.run(max_iter=10) gpgo.getResult() Loading
deepchem/hyper/GP_hyperparameter_optimization.py 0 → 100644 +153 −0 Original line number Diff line number Diff line #!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Fri Jul 28 17:30:59 2017 @author: zqwu """ from __future__ import print_function from __future__ import division from __future__ import unicode_literals import numpy as np import deepchem from deepchem.molnet.preset_hyper_parameters import hps import csv import copy from deepchem.utils.dependencies import pyGPGO_covfunc, pyGPGO_acquisition, \ pyGPGO_surrogates_GaussianProcess, pyGPGO_GPGO def GaussianProcessHyperparamOpt(dataset='tox21', model='tf', split='random', max_iter=20, search_range=4, hp_invalid_list=[ 'seed', 'nb_epoch', 'penalty_type', 'dropouts', 'bypass_dropouts', 'n_pair_feat' ]): hyper_parameters = copy.deepcopy(hps[model]) # Extract parameters hp_list = hyper_parameters.keys() for hp in hp_invalid_list: if hp in hp_list: hp_list.remove(hp) 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[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] # Number of parameters n_param = len(hp_list1) + sum([hp[1] for hp in hp_list2]) # Range of optimization param_range = [] for hp in hp_list1: if hyper_parameters[hp].__class__ is int: param_range.append((('int'), [ hyper_parameters[hp] / search_range, hyper_parameters[hp] * search_range ])) else: param_range.append((('cont'), [ hyper_parameters[hp] / search_range, hyper_parameters[hp] * search_range ])) for hp in hp_list2: 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 ]) for i in range(hp[1])]) else: param_range.extend([(('cont'), [ hyper_parameters[hp[0]][i] / search_range, hyper_parameters[hp[0]][i] * search_range ]) for i in range(hp[1])]) # Dummy names param_name = ['l' + format(i, '02d') for i in range(20)] param = dict(zip(param_name[:n_param], param_range)) def f(l00=0, l01=0, l02=0, l03=0, l04=0, l05=0, l06=0, l07=0, l08=0, l09=0, l10=0, l11=0, l12=0, l13=0, l14=0, l15=0, l16=0, l17=0, l18=0, l19=0): args = locals() # Input hyper parameters i = 0 for hp in hp_list1: 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: hyper_parameters[hp[0]] = [ float(args[param_name[j]]) for j in range(i, i + hp[1]) ] if param_range[i][0] == 'int': hyper_parameters[hp[0]] = map(int, hyper_parameters[hp[0]]) i = i + hp[1] print(hyper_parameters) # Run benchmark deepchem.molnet.run_benchmark( [dataset], str(model), split=str(split), hyper_parameters=hyper_parameters, out_path='/tmp') # Read results with open('/tmp/results.csv', 'r') as f: reader = csv.reader(f) # Return valid set performances return float(list(reader)[-1][8]) cov = pyGPGO_covfunc.matern32() gp = pyGPGO_surrogates_GaussianProcess.GaussianProcess(cov) acq = pyGPGO_acquisition.Acquisition(mode='ExpectedImprovement') gpgo = pyGPGO_GPGO.GPGO(gp, acq, f, param) gpgo.run(max_iter=max_iter) hp_opt, valid_performance_opt = gpgo.getResult() # Readout best hyper parameters i = 0 for hp in hp_list1: 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: hyper_parameters[hp[0]] = [ float(hp_opt[param_name[j]]) for j in range(i, i + hp[1]) ] if param_range[i][0] == 'int': hyper_parameters[hp[0]] = map(int, hyper_parameters[hp[0]]) i = i + hp[1] return hyper_parameters, valid_performance_opt
deepchem/hyper/__init__.py +1 −0 Original line number Diff line number Diff line Loading @@ -11,6 +11,7 @@ from functools import reduce from operator import mul from deepchem.utils.evaluate import Evaluator from deepchem.utils.save import log from deepchem.hyper.GP_hyperparameter_optimization import GaussianProcessHyperparamOpt class HyperparamOpt(object): Loading
deepchem/utils/dependencies.py +5 −0 Original line number Diff line number Diff line Loading @@ -18,3 +18,8 @@ xgboost = import_lazy("xgboost") pdbfixer = import_lazy("pdbfixer") simtk = import_lazy("simtk") mdtraj = import_lazy("mdtraj") pyGPGO_covfunc = import_lazy("pyGPGO.covfunc") pyGPGO_acquisition = import_lazy("pyGPGO.acquisition") pyGPGO_surrogates_GaussianProcess = import_lazy( "pyGPGO.surrogates.GaussianProcess") pyGPGO_GPGO = import_lazy("pyGPGO.GPGO")
examples/GPhyperparameterOptimization.pydeleted 100644 → 0 +0 −82 Original line number Diff line number Diff line #!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Fri Jul 28 17:30:59 2017 @author: zqwu """ from __future__ import print_function from __future__ import division from __future__ import unicode_literals import numpy as np np.random.seed(123) import tensorflow as tf tf.set_random_seed(123) import deepchem as dc from deepchem.molnet.preset_hyper_parameters import hps import csv from pyGPGO.covfunc import matern32 from pyGPGO.acquisition import Acquisition from pyGPGO.surrogates.GaussianProcess import GaussianProcess from pyGPGO.GPGO import GPGO model = 'graphconvreg' dataset = 'delaney' split = 'random' hyper_parameters = hps[model] # Extract parameters hp_list = hyper_parameters.keys() hp_list.remove('seed') hp_list.remove('nb_epoch') hp_list_class = [hyper_parameters[hp].__class__ for hp in hp_list] int_cont = [] for hp in hp_list_class: if hp is int: int_cont.append('int') else: int_cont.append('cont') # Range of optimization param_range = [(int_cont[i], [ hyper_parameters[hp_list[i]] / 4, hyper_parameters[hp_list[i]] * 4 ]) for i in range(len(int_cont))] # Number of parameters n_param = len(param_range) # Dummy names param_name = ['l' + str(i) for i in range(10)] param = dict(zip(param_name[:n_param], param_range)) def f(l0=0, l1=0, l2=0, l3=0, l4=0, l5=0, l6=0, l7=0, l8=0, l9=0): args = locals() keys = args.keys() keys.sort() # Input hyper parameters for i, hp in enumerate(hp_list): hyper_parameters[hp] = args[keys[i]] if int_cont[i] == 'int': hyper_parameters[hp] = int(hyper_parameters[hp]) print(hyper_parameters) # Run benchmark dc.molnet.run_benchmark( [dataset], str(model), split=str(split), hyper_parameters=hyper_parameters, out_path='/tmp') # Read results with open('/tmp/results.csv', 'r') as f: reader = csv.reader(f) return float(list(reader)[-1][8]) cov = matern32() gp = GaussianProcess(cov) acq = Acquisition(mode='ExpectedImprovement') gpgo = GPGO(gp, acq, f, param) gpgo.run(max_iter=10) gpgo.getResult()
