Merge pull request #803 from peastman/imports (4ffe2c9c) · Commits · 钟慕尧 / deepchem

deepchem/dock/tests/test_binding_pocket.py

+12 −11

Original line number	Diff line number	Diff line
		@@ -10,7 +10,6 @@ __copyright__ = "Copyright 2016, Stanford University"
		__license__ = "MIT"

		import sys
		from deepchem.utils.dependencies import mdtraj as md
		import unittest
		import os
		import numpy as np
		@@ -86,8 +85,8 @@ class TestBindingPocket(unittest.TestCase):
		box2 = ((1, 3), (1, 3), (1, 3))
		mapping = {box1: [1, 2, 3, 4], box2: [1, 2, 3, 4, 5]}
		boxes = [box1, box2]
		merged_boxes, _ = dc.dock.binding_pocket.merge_overlapping_boxes(mapping,
		boxes)
		merged_boxes, _ = dc.dock.binding_pocket.merge_overlapping_boxes(
		mapping, boxes)
		print("merged_boxes")
		print(merged_boxes)
		assert len(merged_boxes) == 1
		@@ -98,8 +97,8 @@ class TestBindingPocket(unittest.TestCase):
		box2 = ((1, 2), (1, 2), (1, 2))
		mapping = {box1: [1, 2, 3, 4, 5, 6], box2: [1, 2, 3, 4]}
		boxes = [box1, box2]
		merged_boxes, _ = dc.dock.binding_pocket.merge_overlapping_boxes(mapping,
		boxes)
		merged_boxes, _ = dc.dock.binding_pocket.merge_overlapping_boxes(
		mapping, boxes)
		print("merged_boxes")
		print(merged_boxes)
		assert len(merged_boxes) == 1
		@@ -114,8 +113,8 @@ class TestBindingPocket(unittest.TestCase):
		box2: [1, 2, 3, 4],
		box3: [1, 2, 3, 4, 5]
		}
		merged_boxes, _ = dc.dock.binding_pocket.merge_overlapping_boxes(mapping,
		boxes)
		merged_boxes, _ = dc.dock.binding_pocket.merge_overlapping_boxes(
		mapping, boxes)
		print("merged_boxes")
		print(merged_boxes)
		assert len(merged_boxes) == 1
		@@ -127,12 +126,13 @@ class TestBindingPocket(unittest.TestCase):
		protein_file = os.path.join(current_dir, "1jld_protein.pdb")
		ligand_file = os.path.join(current_dir, "1jld_ligand.sdf")

		import mdtraj as md
		protein = md.load(protein_file)

		finder = dc.dock.ConvexHullPocketFinder()
		all_pockets = finder.find_all_pockets(protein_file)
		pockets, pocket_atoms_map, pocket_coords = finder.find_pockets(protein_file,
		ligand_file)
		pockets, pocket_atoms_map, pocket_coords = finder.find_pockets(
		protein_file, ligand_file)
		# Test that every atom in pocket maps exists
		n_protein_atoms = protein.xyz.shape[1]
		print("protein.xyz.shape")
		@@ -158,11 +158,12 @@ class TestBindingPocket(unittest.TestCase):
		protein_file = os.path.join(current_dir, "1jld_protein.pdb")
		ligand_file = os.path.join(current_dir, "1jld_ligand.sdf")

		import mdtraj as md
		protein = md.load(protein_file)

		finder = dc.dock.RFConvexHullPocketFinder()
		pockets, pocket_atoms_map, pocket_coords = finder.find_pockets(protein_file,
		ligand_file)
		pockets, pocket_atoms_map, pocket_coords = finder.find_pockets(
		protein_file, ligand_file)
		# Test that every atom in pocket maps exists
		n_protein_atoms = protein.xyz.shape[1]
		print("protein.xyz.shape")

deepchem/feat/atomic_coordinates.py

+4 −3

Original line number	Diff line number	Diff line
		@@ -10,7 +10,6 @@ __copyright__ = "Copyright 2016, Stanford University"
		__license__ = "LGPL v2.1+"

		import numpy as np
		from deepchem.utils.dependencies import mdtraj
		from deepchem.feat import Featurizer
		from deepchem.feat import ComplexFeaturizer
		from deepchem.utils import rdkit_util, pad_array
		@@ -56,6 +55,7 @@ def compute_neighbor_list(coords, neighbor_cutoff, max_num_neighbors,
		periodic_box_size):
		"""Computes a neighbor list from atom coordinates."""
		N = coords.shape[0]
		import mdtraj
		traj = mdtraj.Trajectory(coords.reshape((1, N, 3)), None)
		box_size = None
		if periodic_box_size is not None:
		@@ -73,8 +73,9 @@ def compute_neighbor_list(coords, neighbor_cutoff, max_num_neighbors,
		dist = np.linalg.norm(delta, axis=1)
		sorted_neighbors = list(zip(dist, neighbors[i]))
		sorted_neighbors.sort()
		neighbor_list[
		i] = [sorted_neighbors[j][1] for j in range(max_num_neighbors)]
		neighbor_list[i] = [
		sorted_neighbors[j][1] for j in range(max_num_neighbors)
		]
		else:
		neighbor_list[i] = list(neighbors[i])
		return neighbor_list

deepchem/feat/binding_pocket_features.py

+2 −2

Original line number	Diff line number	Diff line
		@@ -10,7 +10,6 @@ __copyright__ = "Copyright 2017, Stanford University"
		__license__ = "MIT"

		import numpy as np
		from deepchem.utils.dependencies import mdtraj as md
		from deepchem.utils.save import log
		from deepchem.feat import Featurizer

		@@ -37,7 +36,8 @@ class BindingPocketFeaturizer(Featurizer):
		"""
		Calculate atomic coodinates.
		"""
		protein = md.load(protein_file)
		import mdtraj
		protein = mdtraj.load(protein_file)
		n_pockets = len(pockets)
		n_residues = len(BindingPocketFeaturizer.residues)
		res_map = dict(zip(BindingPocketFeaturizer.residues, range(n_residues)))

deepchem/hyper/gaussian_process.py

+5 −6

Original line number	Diff line number	Diff line
		@@ -10,8 +10,6 @@ import tempfile
		from deepchem.hyper.grid_search import HyperparamOpt
		from deepchem.utils.evaluate import Evaluator
		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):
		@@ -211,10 +209,11 @@ class GaussianProcessHyperparamOpt(HyperparamOpt):
		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)
		import pyGPGO
		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()

deepchem/models/tests/test_generalize.py

+4 −1

Original line number	Diff line number	Diff line
		@@ -19,7 +19,6 @@ import deepchem as dc
		from sklearn.ensemble import RandomForestRegressor
		from sklearn.linear_model import LinearRegression
		from sklearn.linear_model import LogisticRegression
		from deepchem.utils.dependencies import xgboost


		class TestGeneralize(unittest.TestCase):
		@@ -191,6 +190,7 @@ class TestGeneralize(unittest.TestCase):
		# assert score > .5

		def test_xgboost_regression(self):
		import xgboost
		np.random.seed(123)

		dataset = sklearn.datasets.load_diabetes()
		@@ -206,6 +206,7 @@ class TestGeneralize(unittest.TestCase):
		regression_metric = dc.metrics.Metric(dc.metrics.mae_score)
		# Set early stopping round = n_estimators so that esr won't work
		esr = {'early_stopping_rounds': 50}

		xgb_model = xgboost.XGBRegressor(n_estimators=50, seed=123)
		model = dc.models.XGBoostModel(xgb_model, verbose=False, **esr)

		@@ -219,6 +220,7 @@ class TestGeneralize(unittest.TestCase):

		def test_xgboost_multitask_regression(self):
		"""Test that xgboost models can learn on simple multitask regression."""
		import xgboost
		np.random.seed(123)
		n_tasks = 4
		tasks = range(n_tasks)
		@@ -255,6 +257,7 @@ class TestGeneralize(unittest.TestCase):

		def test_xgboost_classification(self):
		"""Test that sklearn models can learn on simple classification datasets."""
		import xgboost
		np.random.seed(123)
		dataset = sklearn.datasets.load_digits(n_class=2)
		X, y = dataset.data, dataset.target

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