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 +3 −1 Original line number Diff line number Diff line Loading @@ -12,7 +12,6 @@ from operator import mul from deepchem.utils.evaluate import Evaluator from deepchem.utils.save import log class HyperparamOpt(object): """ Provides simple hyperparameter search capabilities. Loading Loading @@ -111,3 +110,6 @@ class HyperparamOpt(object): log("train_score: %f" % train_score, self.verbose) log("validation_score: %f" % best_validation_score, self.verbose) return best_model, best_hyperparams, all_scores from deepchem.hyper.gaussian_process import GaussianProcessHyperparamOpt No newline at end of file deepchem/hyper/gaussian_process.py 0 → 100644 +167 −0 Original line number Diff line number Diff line """ Contains basic hyperparameter optimizations. """ import numpy as np import os import itertools import tempfile import shutil import collections from functools import reduce from operator import mul from deepchem.utils.evaluate import Evaluator from deepchem.utils.save import log from deepchem.hyper import HyperparamOpt from deepchem.molnet.run_benchmark_models import benchmark_classification, benchmark_regression class GaussianProcessHyperparamOpt(HyperparamOpt): """ Gaussian Process Global Optimization(GPGO) """ def hyperparam_search(self, params_dict, train_dataset, valid_dataset, output_transformers, metric, n_features=1024, n_tasks=1, max_iter=20, search_range=4, hp_invalid_list=[ 'seed', 'nb_epoch', 'penalty_type', 'dropouts', 'bypass_dropouts', 'n_pair_feat' ], logdir=None): assert len(metric) == 1, 'Only use one metric' hyper_parameters = params_dict 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 if isinstance(self.model_class, str) or isinstance(self.model_class, unicode): try: train_scores, valid_scores, _ = benchmark_classification( train_dataset, valid_dataset, None, ['task_placeholder']*n_tasks, output_transformers, n_features, metric, model, hyper_parameters=hyper_parameters) except AssertionError: train_scores, valid_scores, _ = benchmark_regression( train_dataset, valid_dataset, None, ['task_placeholder']*n_tasks, output_transformers, n_features, metric, model, hyper_parameters=hyper_parameters) return valid_scores[model][metric[0].name] else: model_dir = tempfile.mkdtemp() model = self.model_class(hyper_parameters, model_dir) model.fit(train_dataset, **hyper_parameters) model.save() evaluator = Evaluator(model, valid_dataset, output_transformers) multitask_scores = evaluator.compute_model_performance([metric]) return multitask_scores[metric.name] 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/utils/dependencies.py +6 −1 Original line number Diff line number Diff line Loading @@ -10,7 +10,7 @@ def import_lazy(module_name): return module except: print("Warning: No %s installed on your system" % module_name) print("Attempting to run %s will throw runtime errors") print("Attempting to run %s will throw runtime errors" % module_name) return None 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/benchmark_hyperparam_search.pydeleted 100644 → 0 +0 −119 Original line number Diff line number Diff line # -*- coding: utf-8 -*- """ Created on Mon Nov 07 22:41:34 2016 @author: Zhenqin Wu """ import numpy as np import deepchem as dc import os import time import sys from scipy.stats import truncnorm # main layer layer_sizes_0 = [1200] weight_init_stddevs_0 = [0.02] bias_init_consts_0 = [1.] dropouts_0 = [0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7] #bypass layer bypass_layer_sizes_0 = truncnorm(-1, 1, loc=200, scale=150) bypass_weight_init_stddevs_0 = [.02] bypass_bias_init_consts_0 = [1.] bypass_dropouts_0 = [0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7] #penalty penalty_0 = truncnorm(-1, 1, loc=0.3, scale=0.2) penalty_type_0 = ['l2'] #general figure batch_size_0 = [50] nb_epoch_0 = [12] #learning rate learning_rate_0 = truncnorm(-1, 1, loc=-3.2, scale=1.2) #for graph-conv and random forest n_filters_0 = [64, 96, 128] n_fully_connected_nodes_0 = [100, 120, 140, 160, 200, 240, 300] n_estimators_0 = [500] seed = None out_path = '.' dname = sys.argv[1] model = sys.argv[2] parameters_printed = { 'tf': [ 'layer_sizes', 'weight_init_stddevs', 'bias_init_consts', 'dropouts', 'penalty', 'penalty_type', 'batch_size', 'nb_epoch', 'learning_rate' ], 'tf_robust': [ 'layer_sizes', 'weight_init_stddevs', 'bias_init_consts', 'dropouts', 'bypass_layer_sizes', 'bypass_weight_init_stddevs', 'bypass_bias_init_consts', 'bypass_dropouts', 'penalty', 'penalty_type', 'batch_size', 'nb_epoch', 'learning_rate' ], 'logreg': ['penalty', 'penalty_type', 'batch_size', 'nb_epoch', 'learning_rate'], 'graphconv': [ 'batch_size', 'nb_epoch', 'learning_rate', 'n_filters', 'n_fully_connected_nodes' ], 'rf': ['n_estimators'] } hps = {} for i in range(int(sys.argv[3])): layer_sizes = layer_sizes_0 weight_init_stddevs = weight_init_stddevs_0 bias_init_consts = bias_init_consts_0 dropouts = [np.random.choice(dropouts_0)] bypass_layer_sizes = [int(bypass_layer_sizes_0.rvs())] bypass_weight_init_stddevs = bypass_weight_init_stddevs_0 bypass_bias_init_consts = bypass_bias_init_consts_0 bypass_dropouts = [np.random.choice(bypass_dropouts_0)] penalty = penalty_0.rvs() penalty_type = np.random.choice(penalty_type_0) batch_size = np.random.choice(batch_size_0) nb_epoch = np.random.choice(nb_epoch_0) learning_rate = 10**(learning_rate_0.rvs()) n_filters = np.random.choice(n_filters_0) n_fully_connected_nodes = np.random.choice(n_fully_connected_nodes_0) n_estimators = np.random.choice(n_estimators_0) hps[model] = { 'layer_sizes': layer_sizes, 'weight_init_stddevs': weight_init_stddevs, 'bias_init_consts': bias_init_consts, 'dropouts': dropouts, 'bypass_layer_sizes': bypass_layer_sizes, 'bypass_weight_init_stddevs': bypass_weight_init_stddevs, 'bypass_bias_init_consts': bypass_bias_init_consts, 'bypass_dropouts': bypass_dropouts, 'penalty': penalty, 'penalty_type': penalty_type, 'batch_size': batch_size, 'nb_epoch': nb_epoch, 'learning_rate': learning_rate, 'n_filters': n_filters, 'n_fully_connected_nodes': n_fully_connected_nodes, 'n_estimators': n_estimators, 'seed': seed } with open(os.path.join(out_path, 'hps.csv'), 'a') as f: f.write('\n' + str(i) + ',' + dname + ',') for item in hps[model][i]: if item in parameters_printed[model]: f.write(item + ',') f.write(str(hps[model][i][item]) + ',') dc.molnet.run_benchmark( [dname], str(model), out_path=out_path, hyper_parameters=hps) 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 +3 −1 Original line number Diff line number Diff line Loading @@ -12,7 +12,6 @@ from operator import mul from deepchem.utils.evaluate import Evaluator from deepchem.utils.save import log class HyperparamOpt(object): """ Provides simple hyperparameter search capabilities. Loading Loading @@ -111,3 +110,6 @@ class HyperparamOpt(object): log("train_score: %f" % train_score, self.verbose) log("validation_score: %f" % best_validation_score, self.verbose) return best_model, best_hyperparams, all_scores from deepchem.hyper.gaussian_process import GaussianProcessHyperparamOpt No newline at end of file
deepchem/hyper/gaussian_process.py 0 → 100644 +167 −0 Original line number Diff line number Diff line """ Contains basic hyperparameter optimizations. """ import numpy as np import os import itertools import tempfile import shutil import collections from functools import reduce from operator import mul from deepchem.utils.evaluate import Evaluator from deepchem.utils.save import log from deepchem.hyper import HyperparamOpt from deepchem.molnet.run_benchmark_models import benchmark_classification, benchmark_regression class GaussianProcessHyperparamOpt(HyperparamOpt): """ Gaussian Process Global Optimization(GPGO) """ def hyperparam_search(self, params_dict, train_dataset, valid_dataset, output_transformers, metric, n_features=1024, n_tasks=1, max_iter=20, search_range=4, hp_invalid_list=[ 'seed', 'nb_epoch', 'penalty_type', 'dropouts', 'bypass_dropouts', 'n_pair_feat' ], logdir=None): assert len(metric) == 1, 'Only use one metric' hyper_parameters = params_dict 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 if isinstance(self.model_class, str) or isinstance(self.model_class, unicode): try: train_scores, valid_scores, _ = benchmark_classification( train_dataset, valid_dataset, None, ['task_placeholder']*n_tasks, output_transformers, n_features, metric, model, hyper_parameters=hyper_parameters) except AssertionError: train_scores, valid_scores, _ = benchmark_regression( train_dataset, valid_dataset, None, ['task_placeholder']*n_tasks, output_transformers, n_features, metric, model, hyper_parameters=hyper_parameters) return valid_scores[model][metric[0].name] else: model_dir = tempfile.mkdtemp() model = self.model_class(hyper_parameters, model_dir) model.fit(train_dataset, **hyper_parameters) model.save() evaluator = Evaluator(model, valid_dataset, output_transformers) multitask_scores = evaluator.compute_model_performance([metric]) return multitask_scores[metric.name] 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/utils/dependencies.py +6 −1 Original line number Diff line number Diff line Loading @@ -10,7 +10,7 @@ def import_lazy(module_name): return module except: print("Warning: No %s installed on your system" % module_name) print("Attempting to run %s will throw runtime errors") print("Attempting to run %s will throw runtime errors" % module_name) return None 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/benchmark_hyperparam_search.pydeleted 100644 → 0 +0 −119 Original line number Diff line number Diff line # -*- coding: utf-8 -*- """ Created on Mon Nov 07 22:41:34 2016 @author: Zhenqin Wu """ import numpy as np import deepchem as dc import os import time import sys from scipy.stats import truncnorm # main layer layer_sizes_0 = [1200] weight_init_stddevs_0 = [0.02] bias_init_consts_0 = [1.] dropouts_0 = [0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7] #bypass layer bypass_layer_sizes_0 = truncnorm(-1, 1, loc=200, scale=150) bypass_weight_init_stddevs_0 = [.02] bypass_bias_init_consts_0 = [1.] bypass_dropouts_0 = [0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7] #penalty penalty_0 = truncnorm(-1, 1, loc=0.3, scale=0.2) penalty_type_0 = ['l2'] #general figure batch_size_0 = [50] nb_epoch_0 = [12] #learning rate learning_rate_0 = truncnorm(-1, 1, loc=-3.2, scale=1.2) #for graph-conv and random forest n_filters_0 = [64, 96, 128] n_fully_connected_nodes_0 = [100, 120, 140, 160, 200, 240, 300] n_estimators_0 = [500] seed = None out_path = '.' dname = sys.argv[1] model = sys.argv[2] parameters_printed = { 'tf': [ 'layer_sizes', 'weight_init_stddevs', 'bias_init_consts', 'dropouts', 'penalty', 'penalty_type', 'batch_size', 'nb_epoch', 'learning_rate' ], 'tf_robust': [ 'layer_sizes', 'weight_init_stddevs', 'bias_init_consts', 'dropouts', 'bypass_layer_sizes', 'bypass_weight_init_stddevs', 'bypass_bias_init_consts', 'bypass_dropouts', 'penalty', 'penalty_type', 'batch_size', 'nb_epoch', 'learning_rate' ], 'logreg': ['penalty', 'penalty_type', 'batch_size', 'nb_epoch', 'learning_rate'], 'graphconv': [ 'batch_size', 'nb_epoch', 'learning_rate', 'n_filters', 'n_fully_connected_nodes' ], 'rf': ['n_estimators'] } hps = {} for i in range(int(sys.argv[3])): layer_sizes = layer_sizes_0 weight_init_stddevs = weight_init_stddevs_0 bias_init_consts = bias_init_consts_0 dropouts = [np.random.choice(dropouts_0)] bypass_layer_sizes = [int(bypass_layer_sizes_0.rvs())] bypass_weight_init_stddevs = bypass_weight_init_stddevs_0 bypass_bias_init_consts = bypass_bias_init_consts_0 bypass_dropouts = [np.random.choice(bypass_dropouts_0)] penalty = penalty_0.rvs() penalty_type = np.random.choice(penalty_type_0) batch_size = np.random.choice(batch_size_0) nb_epoch = np.random.choice(nb_epoch_0) learning_rate = 10**(learning_rate_0.rvs()) n_filters = np.random.choice(n_filters_0) n_fully_connected_nodes = np.random.choice(n_fully_connected_nodes_0) n_estimators = np.random.choice(n_estimators_0) hps[model] = { 'layer_sizes': layer_sizes, 'weight_init_stddevs': weight_init_stddevs, 'bias_init_consts': bias_init_consts, 'dropouts': dropouts, 'bypass_layer_sizes': bypass_layer_sizes, 'bypass_weight_init_stddevs': bypass_weight_init_stddevs, 'bypass_bias_init_consts': bypass_bias_init_consts, 'bypass_dropouts': bypass_dropouts, 'penalty': penalty, 'penalty_type': penalty_type, 'batch_size': batch_size, 'nb_epoch': nb_epoch, 'learning_rate': learning_rate, 'n_filters': n_filters, 'n_fully_connected_nodes': n_fully_connected_nodes, 'n_estimators': n_estimators, 'seed': seed } with open(os.path.join(out_path, 'hps.csv'), 'a') as f: f.write('\n' + str(i) + ',' + dname + ',') for item in hps[model][i]: if item in parameters_printed[model]: f.write(item + ',') f.write(str(hps[model][i][item]) + ',') dc.molnet.run_benchmark( [dname], str(model), out_path=out_path, hyper_parameters=hps)
