Loading contrib/atomicconv/acnn/core/get_acnn_core.sh 0 → 100755 +4 −0 Original line number Diff line number Diff line echo "Pulling featurized and split ACNN datasets from deepchem" wget http://deepchem.io.s3-website-us-west-1.amazonaws.com/featurized_datasets/acnn_core.tar.gz echo "Extracting ACNN datasets" tar -zxvf acnn_core.tar.gz contrib/atomicconv/acnn/core/opt_random.py 0 → 100644 +92 −0 Original line number Diff line number Diff line from __future__ import print_function from __future__ import division from __future__ import unicode_literals __author__ = "Joseph Gomes" __copyright__ = "Copyright 2017, Stanford University" __license__ = "MIT" import os import sys import deepchem as dc import numpy as np import tensorflow as tf sys.path.append("../../models") from atomicnet_ops import create_symmetry_parameters from atomicnet import TensorflowFragmentRegressor seed = 123 np.random.seed(seed) tf.set_random_seed(seed) base_dir = os.getcwd() data_dir = os.path.join(base_dir, "datasets") train_dir = os.path.join(data_dir, "random_train") test_dir = os.path.join(data_dir, "random_test") model_dir = os.path.join(base_dir, "random_model") frag1_num_atoms = 140 frag2_num_atoms = 821 complex_num_atoms = 908 max_num_neighbors = 12 neighbor_cutoff = 12.0 train_dataset = dc.data.DiskDataset(train_dir) test_dataset = dc.data.DiskDataset(test_dir) pdbbind_tasks = ["-logKd/Ki"] transformers = [ dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset) ] for transformer in transformers: train_dataset = transformer.transform(train_dataset) test_dataset = transformer.transform(test_dataset) at = [1., 6, 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.] radial = [[12.0], [0.0, 4.0, 8.0], [4.0]] rp = create_symmetry_parameters(radial) layer_sizes = [32, 32, 16] weight_init_stddevs = [ 1 / np.sqrt(layer_sizes[0]), 1 / np.sqrt(layer_sizes[1]), 1 / np.sqrt(layer_sizes[2]) ] dropouts = [0., 0., 0.] penalty_type = "l2" penalty = 0. model = TensorflowFragmentRegressor( len(pdbbind_tasks), rp, at, frag1_num_atoms, frag2_num_atoms, complex_num_atoms, max_num_neighbors, logdir=model_dir, layer_sizes=layer_sizes, weight_init_stddevs=weight_init_stddevs, bias_init_consts=[0., 0., 0.], penalty=penalty, penalty_type=penalty_type, dropouts=dropouts, learning_rate=0.002, momentum=0.8, optimizer="adam", batch_size=24, conv_layers=1, boxsize=None, verbose=True, seed=seed) model.fit(train_dataset, nb_epoch=100) metric = [ dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"), dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression") ] train_evaluator = dc.utils.evaluate.Evaluator(model, train_dataset, transformers) train_scores = train_evaluator.compute_model_performance(metric) print("Train scores") print(train_scores) test_evaluator = dc.utils.evaluate.Evaluator(model, test_dataset, transformers) test_scores = test_evaluator.compute_model_performance(metric) print("Test scores") print(test_scores) contrib/atomicconv/acnn/core/opt_scaffold.py 0 → 100644 +92 −0 Original line number Diff line number Diff line from __future__ import print_function from __future__ import division from __future__ import unicode_literals __author__ = "Joseph Gomes" __copyright__ = "Copyright 2017, Stanford University" __license__ = "MIT" import os import sys import deepchem as dc import numpy as np import tensorflow as tf sys.path.append("../../models") from atomicnet_ops import create_symmetry_parameters from atomicnet import TensorflowFragmentRegressor seed = 123 np.random.seed(seed) tf.set_random_seed(seed) base_dir = os.getcwd() data_dir = os.path.join(base_dir, "datasets") train_dir = os.path.join(data_dir, "scaffold_train") test_dir = os.path.join(data_dir, "scaffold_test") model_dir = os.path.join(base_dir, "scaffold_model") frag1_num_atoms = 140 frag2_num_atoms = 821 complex_num_atoms = 908 max_num_neighbors = 12 neighbor_cutoff = 12.0 train_dataset = dc.data.DiskDataset(train_dir) test_dataset = dc.data.DiskDataset(test_dir) pdbbind_tasks = ["-logKd/Ki"] transformers = [ dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset) ] for transformer in transformers: train_dataset = transformer.transform(train_dataset) test_dataset = transformer.transform(test_dataset) at = [1., 6, 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.] radial = [[12.0], [0.0, 4.0, 8.0], [4.0]] rp = create_symmetry_parameters(radial) layer_sizes = [32, 32, 16] weight_init_stddevs = [ 1 / np.sqrt(layer_sizes[0]), 1 / np.sqrt(layer_sizes[1]), 1 / np.sqrt(layer_sizes[2]) ] dropouts = [0., 0., 0.] penalty_type = "l2" penalty = 0. model = TensorflowFragmentRegressor( len(pdbbind_tasks), rp, at, frag1_num_atoms, frag2_num_atoms, complex_num_atoms, max_num_neighbors, logdir=model_dir, layer_sizes=layer_sizes, weight_init_stddevs=weight_init_stddevs, bias_init_consts=[0., 0., 0.], penalty=penalty, penalty_type=penalty_type, dropouts=dropouts, learning_rate=0.002, momentum=0.8, optimizer="adam", batch_size=24, conv_layers=1, boxsize=None, verbose=True, seed=seed) model.fit(train_dataset, nb_epoch=100) metric = [ dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"), dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression") ] train_evaluator = dc.utils.evaluate.Evaluator(model, train_dataset, transformers) train_scores = train_evaluator.compute_model_performance(metric) print("Train scores") print(train_scores) test_evaluator = dc.utils.evaluate.Evaluator(model, test_dataset, transformers) test_scores = test_evaluator.compute_model_performance(metric) print("Test scores") print(test_scores) contrib/atomicconv/acnn/core/opt_stratified.py 0 → 100644 +92 −0 Original line number Diff line number Diff line from __future__ import print_function from __future__ import division from __future__ import unicode_literals __author__ = "Joseph Gomes" __copyright__ = "Copyright 2017, Stanford University" __license__ = "MIT" import os import sys import deepchem as dc import numpy as np import tensorflow as tf sys.path.append("../../models") from atomicnet_ops import create_symmetry_parameters from atomicnet import TensorflowFragmentRegressor seed = 123 np.random.seed(seed) tf.set_random_seed(seed) base_dir = os.getcwd() data_dir = os.path.join(base_dir, "datasets") train_dir = os.path.join(data_dir, "stratified_train") test_dir = os.path.join(data_dir, "stratified_test") model_dir = os.path.join(base_dir, "stratified_model") frag1_num_atoms = 140 frag2_num_atoms = 821 complex_num_atoms = 908 max_num_neighbors = 12 neighbor_cutoff = 12.0 train_dataset = dc.data.DiskDataset(train_dir) test_dataset = dc.data.DiskDataset(test_dir) pdbbind_tasks = ["-logKd/Ki"] transformers = [ dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset) ] for transformer in transformers: train_dataset = transformer.transform(train_dataset) test_dataset = transformer.transform(test_dataset) at = [1., 6, 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.] radial = [[12.0], [0.0, 4.0, 8.0], [4.0]] rp = create_symmetry_parameters(radial) layer_sizes = [32, 32, 16] weight_init_stddevs = [ 1 / np.sqrt(layer_sizes[0]), 1 / np.sqrt(layer_sizes[1]), 1 / np.sqrt(layer_sizes[2]) ] dropouts = [0., 0., 0.] penalty_type = "l2" penalty = 0. model = TensorflowFragmentRegressor( len(pdbbind_tasks), rp, at, frag1_num_atoms, frag2_num_atoms, complex_num_atoms, max_num_neighbors, logdir=model_dir, layer_sizes=layer_sizes, weight_init_stddevs=weight_init_stddevs, bias_init_consts=[0., 0., 0.], penalty=penalty, penalty_type=penalty_type, dropouts=dropouts, learning_rate=0.002, momentum=0.8, optimizer="adam", batch_size=24, conv_layers=1, boxsize=None, verbose=True, seed=seed) model.fit(train_dataset, nb_epoch=100) metric = [ dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"), dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression") ] train_evaluator = dc.utils.evaluate.Evaluator(model, train_dataset, transformers) train_scores = train_evaluator.compute_model_performance(metric) print("Train scores") print(train_scores) test_evaluator = dc.utils.evaluate.Evaluator(model, test_dataset, transformers) test_scores = test_evaluator.compute_model_performance(metric) print("Test scores") print(test_scores) contrib/atomicconv/acnn/core/opt_temporal.py 0 → 100644 +92 −0 Original line number Diff line number Diff line from __future__ import print_function from __future__ import division from __future__ import unicode_literals __author__ = "Joseph Gomes" __copyright__ = "Copyright 2017, Stanford University" __license__ = "MIT" import os import sys import deepchem as dc import numpy as np import tensorflow as tf sys.path.append("../../models") from atomicnet_ops import create_symmetry_parameters from atomicnet import TensorflowFragmentRegressor seed = 123 np.random.seed(seed) tf.set_random_seed(seed) base_dir = os.getcwd() data_dir = os.path.join(base_dir, "datasets") train_dir = os.path.join(data_dir, "temporal_train") test_dir = os.path.join(data_dir, "temporal_test") model_dir = os.path.join(base_dir, "temporal_model") frag1_num_atoms = 140 frag2_num_atoms = 821 complex_num_atoms = 908 max_num_neighbors = 12 neighbor_cutoff = 12.0 train_dataset = dc.data.DiskDataset(train_dir) test_dataset = dc.data.DiskDataset(test_dir) pdbbind_tasks = ["-logKd/Ki"] transformers = [ dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset) ] for transformer in transformers: train_dataset = transformer.transform(train_dataset) test_dataset = transformer.transform(test_dataset) at = [1., 6, 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.] radial = [[12.0], [0.0, 4.0, 8.0], [4.0]] rp = create_symmetry_parameters(radial) layer_sizes = [32, 32, 16] weight_init_stddevs = [ 1 / np.sqrt(layer_sizes[0]), 1 / np.sqrt(layer_sizes[1]), 1 / np.sqrt(layer_sizes[2]) ] dropouts = [0., 0., 0.] penalty_type = "l2" penalty = 0. model = TensorflowFragmentRegressor( len(pdbbind_tasks), rp, at, frag1_num_atoms, frag2_num_atoms, complex_num_atoms, max_num_neighbors, logdir=model_dir, layer_sizes=layer_sizes, weight_init_stddevs=weight_init_stddevs, bias_init_consts=[0., 0., 0.], penalty=penalty, penalty_type=penalty_type, dropouts=dropouts, learning_rate=0.002, momentum=0.8, optimizer="adam", batch_size=24, conv_layers=1, boxsize=None, verbose=True, seed=seed) model.fit(train_dataset, nb_epoch=100) metric = [ dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"), dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression") ] train_evaluator = dc.utils.evaluate.Evaluator(model, train_dataset, transformers) train_scores = train_evaluator.compute_model_performance(metric) print("Train scores") print(train_scores) test_evaluator = dc.utils.evaluate.Evaluator(model, test_dataset, transformers) test_scores = test_evaluator.compute_model_performance(metric) print("Test scores") print(test_scores) Loading
contrib/atomicconv/acnn/core/get_acnn_core.sh 0 → 100755 +4 −0 Original line number Diff line number Diff line echo "Pulling featurized and split ACNN datasets from deepchem" wget http://deepchem.io.s3-website-us-west-1.amazonaws.com/featurized_datasets/acnn_core.tar.gz echo "Extracting ACNN datasets" tar -zxvf acnn_core.tar.gz
contrib/atomicconv/acnn/core/opt_random.py 0 → 100644 +92 −0 Original line number Diff line number Diff line from __future__ import print_function from __future__ import division from __future__ import unicode_literals __author__ = "Joseph Gomes" __copyright__ = "Copyright 2017, Stanford University" __license__ = "MIT" import os import sys import deepchem as dc import numpy as np import tensorflow as tf sys.path.append("../../models") from atomicnet_ops import create_symmetry_parameters from atomicnet import TensorflowFragmentRegressor seed = 123 np.random.seed(seed) tf.set_random_seed(seed) base_dir = os.getcwd() data_dir = os.path.join(base_dir, "datasets") train_dir = os.path.join(data_dir, "random_train") test_dir = os.path.join(data_dir, "random_test") model_dir = os.path.join(base_dir, "random_model") frag1_num_atoms = 140 frag2_num_atoms = 821 complex_num_atoms = 908 max_num_neighbors = 12 neighbor_cutoff = 12.0 train_dataset = dc.data.DiskDataset(train_dir) test_dataset = dc.data.DiskDataset(test_dir) pdbbind_tasks = ["-logKd/Ki"] transformers = [ dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset) ] for transformer in transformers: train_dataset = transformer.transform(train_dataset) test_dataset = transformer.transform(test_dataset) at = [1., 6, 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.] radial = [[12.0], [0.0, 4.0, 8.0], [4.0]] rp = create_symmetry_parameters(radial) layer_sizes = [32, 32, 16] weight_init_stddevs = [ 1 / np.sqrt(layer_sizes[0]), 1 / np.sqrt(layer_sizes[1]), 1 / np.sqrt(layer_sizes[2]) ] dropouts = [0., 0., 0.] penalty_type = "l2" penalty = 0. model = TensorflowFragmentRegressor( len(pdbbind_tasks), rp, at, frag1_num_atoms, frag2_num_atoms, complex_num_atoms, max_num_neighbors, logdir=model_dir, layer_sizes=layer_sizes, weight_init_stddevs=weight_init_stddevs, bias_init_consts=[0., 0., 0.], penalty=penalty, penalty_type=penalty_type, dropouts=dropouts, learning_rate=0.002, momentum=0.8, optimizer="adam", batch_size=24, conv_layers=1, boxsize=None, verbose=True, seed=seed) model.fit(train_dataset, nb_epoch=100) metric = [ dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"), dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression") ] train_evaluator = dc.utils.evaluate.Evaluator(model, train_dataset, transformers) train_scores = train_evaluator.compute_model_performance(metric) print("Train scores") print(train_scores) test_evaluator = dc.utils.evaluate.Evaluator(model, test_dataset, transformers) test_scores = test_evaluator.compute_model_performance(metric) print("Test scores") print(test_scores)
contrib/atomicconv/acnn/core/opt_scaffold.py 0 → 100644 +92 −0 Original line number Diff line number Diff line from __future__ import print_function from __future__ import division from __future__ import unicode_literals __author__ = "Joseph Gomes" __copyright__ = "Copyright 2017, Stanford University" __license__ = "MIT" import os import sys import deepchem as dc import numpy as np import tensorflow as tf sys.path.append("../../models") from atomicnet_ops import create_symmetry_parameters from atomicnet import TensorflowFragmentRegressor seed = 123 np.random.seed(seed) tf.set_random_seed(seed) base_dir = os.getcwd() data_dir = os.path.join(base_dir, "datasets") train_dir = os.path.join(data_dir, "scaffold_train") test_dir = os.path.join(data_dir, "scaffold_test") model_dir = os.path.join(base_dir, "scaffold_model") frag1_num_atoms = 140 frag2_num_atoms = 821 complex_num_atoms = 908 max_num_neighbors = 12 neighbor_cutoff = 12.0 train_dataset = dc.data.DiskDataset(train_dir) test_dataset = dc.data.DiskDataset(test_dir) pdbbind_tasks = ["-logKd/Ki"] transformers = [ dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset) ] for transformer in transformers: train_dataset = transformer.transform(train_dataset) test_dataset = transformer.transform(test_dataset) at = [1., 6, 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.] radial = [[12.0], [0.0, 4.0, 8.0], [4.0]] rp = create_symmetry_parameters(radial) layer_sizes = [32, 32, 16] weight_init_stddevs = [ 1 / np.sqrt(layer_sizes[0]), 1 / np.sqrt(layer_sizes[1]), 1 / np.sqrt(layer_sizes[2]) ] dropouts = [0., 0., 0.] penalty_type = "l2" penalty = 0. model = TensorflowFragmentRegressor( len(pdbbind_tasks), rp, at, frag1_num_atoms, frag2_num_atoms, complex_num_atoms, max_num_neighbors, logdir=model_dir, layer_sizes=layer_sizes, weight_init_stddevs=weight_init_stddevs, bias_init_consts=[0., 0., 0.], penalty=penalty, penalty_type=penalty_type, dropouts=dropouts, learning_rate=0.002, momentum=0.8, optimizer="adam", batch_size=24, conv_layers=1, boxsize=None, verbose=True, seed=seed) model.fit(train_dataset, nb_epoch=100) metric = [ dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"), dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression") ] train_evaluator = dc.utils.evaluate.Evaluator(model, train_dataset, transformers) train_scores = train_evaluator.compute_model_performance(metric) print("Train scores") print(train_scores) test_evaluator = dc.utils.evaluate.Evaluator(model, test_dataset, transformers) test_scores = test_evaluator.compute_model_performance(metric) print("Test scores") print(test_scores)
contrib/atomicconv/acnn/core/opt_stratified.py 0 → 100644 +92 −0 Original line number Diff line number Diff line from __future__ import print_function from __future__ import division from __future__ import unicode_literals __author__ = "Joseph Gomes" __copyright__ = "Copyright 2017, Stanford University" __license__ = "MIT" import os import sys import deepchem as dc import numpy as np import tensorflow as tf sys.path.append("../../models") from atomicnet_ops import create_symmetry_parameters from atomicnet import TensorflowFragmentRegressor seed = 123 np.random.seed(seed) tf.set_random_seed(seed) base_dir = os.getcwd() data_dir = os.path.join(base_dir, "datasets") train_dir = os.path.join(data_dir, "stratified_train") test_dir = os.path.join(data_dir, "stratified_test") model_dir = os.path.join(base_dir, "stratified_model") frag1_num_atoms = 140 frag2_num_atoms = 821 complex_num_atoms = 908 max_num_neighbors = 12 neighbor_cutoff = 12.0 train_dataset = dc.data.DiskDataset(train_dir) test_dataset = dc.data.DiskDataset(test_dir) pdbbind_tasks = ["-logKd/Ki"] transformers = [ dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset) ] for transformer in transformers: train_dataset = transformer.transform(train_dataset) test_dataset = transformer.transform(test_dataset) at = [1., 6, 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.] radial = [[12.0], [0.0, 4.0, 8.0], [4.0]] rp = create_symmetry_parameters(radial) layer_sizes = [32, 32, 16] weight_init_stddevs = [ 1 / np.sqrt(layer_sizes[0]), 1 / np.sqrt(layer_sizes[1]), 1 / np.sqrt(layer_sizes[2]) ] dropouts = [0., 0., 0.] penalty_type = "l2" penalty = 0. model = TensorflowFragmentRegressor( len(pdbbind_tasks), rp, at, frag1_num_atoms, frag2_num_atoms, complex_num_atoms, max_num_neighbors, logdir=model_dir, layer_sizes=layer_sizes, weight_init_stddevs=weight_init_stddevs, bias_init_consts=[0., 0., 0.], penalty=penalty, penalty_type=penalty_type, dropouts=dropouts, learning_rate=0.002, momentum=0.8, optimizer="adam", batch_size=24, conv_layers=1, boxsize=None, verbose=True, seed=seed) model.fit(train_dataset, nb_epoch=100) metric = [ dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"), dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression") ] train_evaluator = dc.utils.evaluate.Evaluator(model, train_dataset, transformers) train_scores = train_evaluator.compute_model_performance(metric) print("Train scores") print(train_scores) test_evaluator = dc.utils.evaluate.Evaluator(model, test_dataset, transformers) test_scores = test_evaluator.compute_model_performance(metric) print("Test scores") print(test_scores)
contrib/atomicconv/acnn/core/opt_temporal.py 0 → 100644 +92 −0 Original line number Diff line number Diff line from __future__ import print_function from __future__ import division from __future__ import unicode_literals __author__ = "Joseph Gomes" __copyright__ = "Copyright 2017, Stanford University" __license__ = "MIT" import os import sys import deepchem as dc import numpy as np import tensorflow as tf sys.path.append("../../models") from atomicnet_ops import create_symmetry_parameters from atomicnet import TensorflowFragmentRegressor seed = 123 np.random.seed(seed) tf.set_random_seed(seed) base_dir = os.getcwd() data_dir = os.path.join(base_dir, "datasets") train_dir = os.path.join(data_dir, "temporal_train") test_dir = os.path.join(data_dir, "temporal_test") model_dir = os.path.join(base_dir, "temporal_model") frag1_num_atoms = 140 frag2_num_atoms = 821 complex_num_atoms = 908 max_num_neighbors = 12 neighbor_cutoff = 12.0 train_dataset = dc.data.DiskDataset(train_dir) test_dataset = dc.data.DiskDataset(test_dir) pdbbind_tasks = ["-logKd/Ki"] transformers = [ dc.trans.NormalizationTransformer(transform_y=True, dataset=train_dataset) ] for transformer in transformers: train_dataset = transformer.transform(train_dataset) test_dataset = transformer.transform(test_dataset) at = [1., 6, 7., 8., 9., 11., 12., 15., 16., 17., 20., 25., 30., 35., 53.] radial = [[12.0], [0.0, 4.0, 8.0], [4.0]] rp = create_symmetry_parameters(radial) layer_sizes = [32, 32, 16] weight_init_stddevs = [ 1 / np.sqrt(layer_sizes[0]), 1 / np.sqrt(layer_sizes[1]), 1 / np.sqrt(layer_sizes[2]) ] dropouts = [0., 0., 0.] penalty_type = "l2" penalty = 0. model = TensorflowFragmentRegressor( len(pdbbind_tasks), rp, at, frag1_num_atoms, frag2_num_atoms, complex_num_atoms, max_num_neighbors, logdir=model_dir, layer_sizes=layer_sizes, weight_init_stddevs=weight_init_stddevs, bias_init_consts=[0., 0., 0.], penalty=penalty, penalty_type=penalty_type, dropouts=dropouts, learning_rate=0.002, momentum=0.8, optimizer="adam", batch_size=24, conv_layers=1, boxsize=None, verbose=True, seed=seed) model.fit(train_dataset, nb_epoch=100) metric = [ dc.metrics.Metric(dc.metrics.mean_absolute_error, mode="regression"), dc.metrics.Metric(dc.metrics.pearson_r2_score, mode="regression") ] train_evaluator = dc.utils.evaluate.Evaluator(model, train_dataset, transformers) train_scores = train_evaluator.compute_model_performance(metric) print("Train scores") print(train_scores) test_evaluator = dc.utils.evaluate.Evaluator(model, test_dataset, transformers) test_scores = test_evaluator.compute_model_performance(metric) print("Test scores") print(test_scores)
