Preliminary commit of task splits. (c17663cb) · Commits · 钟慕尧 / deepchem

deepchem/datasets/init.py

+15 −15

Original line number	Diff line number	Diff line
		@@ -237,7 +237,7 @@ class NumpyDataset(Dataset):

		def get_task_names(self):
		"""Get the names of the tasks associated with this dataset."""
		tasks = np.arange(self._y.shape[1])
		return np.arange(self._y.shape[1])

		@property
		def X(self):
		@@ -486,7 +486,6 @@ class DiskDataset(Dataset):
		"""
		return self.metadata_df.shape[0]


		def itershards(self):
		"""
		Return an object that iterates over all shards in dataset.
		@@ -603,6 +602,7 @@ class DiskDataset(Dataset):
		@staticmethod
		def from_numpy(data_dir, X, y, w=None, ids=None, tasks=None, verbosity=None,
		compute_feature_statistics=True):
		"""Creates a DiskDataset object from specified Numpy arrays."""
		n_samples = len(X)
		# The -1 indicates that y will be reshaped to have length -1
		if n_samples > 0:

deepchem/splits/init.py

+2 −3

Original line number	Diff line number	Diff line
		@@ -68,8 +68,8 @@ class Splitter(object):
		fold_datasets.append(fold_dataset)
		return fold_datasets

		def train_valid_test_split(self, dataset, train_dir,
		valid_dir, test_dir, frac_train=.8,
		def train_valid_test_split(self, dataset, train_dir=None,
		valid_dir=None, test_dir=None, frac_train=.8,
		frac_valid=.1, frac_test=.1, seed=None,
		log_every_n=1000,
		compute_feature_statistics=True):
		@@ -243,7 +243,6 @@ class RandomStratifiedSplitter(Splitter):
		return fold_datasets



		class MolecularWeightSplitter(Splitter):
		"""
		Class for doing data splits by molecular weight.

deepchem/splits/task_splitter.py

0 → 100644

+77 −0

Original line number	Diff line number	Diff line
		"""
		Contains an abstract base class that supports chemically aware data splits.
		"""
		from __future__ import print_function
		from __future__ import division
		from __future__ import unicode_literals

		__author__ = "Bharath Ramsundar"
		__copyright__ = "Copyright 2016, Stanford University"
		__license__ = "GPL"

		import tempfile
		import numpy as np
		from rdkit import Chem
		from deepchem.utils import ScaffoldGenerator
		from deepchem.utils.save import log
		from deepchem.datasets import NumpyDataset
		from deepchem.featurizers.featurize import load_data
		from deepchem.splits import Splitter

		class TaskSplitter(Splitter):
		"""
		Provides a simple interface for splitting datasets task-wise.

		For some learning problems, the training and test datasets should
		have different tasks entirely. This is a different paradigm from the
		usual Splitter, which ensures that split datasets have different
		datapoints, not different tasks.
		"""

		def __init__(self):
		"Creates Task Splitter object."
		pass

		def train_valid_test_split(self, dataset, frac_train=.8, frac_valid=.1,
		frac_test=.1):
		"""Performs a train/valid/test split of the tasks for dataset.

		Parameters
		----------
		dataset: deepchem.datasets.Dataset
		Dataset to be split
		frac_train: float, optional
		Proportion of tasks to be put into train. Rounded to nearest int.
		frac_valid: float, optional
		Proportion of tasks to be put into valid. Rounded to nearest int.
		frac_test: float, optional
		Proportion of tasks to be put into test. Rounded to nearest int.
		"""
		n_tasks = len(dataset.get_task_names())
		n_train = np.round(frac_train * n_tasks)
		n_valid = np.round(frac_valid * n_tasks)
		n_test = np.round(frac_test * n_tasks)
		if n_train + n_valid + n_test != n_tasks:
		raise ValueError("Train/Valid/Test fractions don't split tasks evenly.")
		########################################### DEBUG
		print("train_valid_test_split")
		print("n_train, n_valid, n_test")
		print(n_train, n_valid, n_test)
		########################################### DEBUG

		X, y, w, ids = dataset.X, dataset.y, dataset.w, dataset.ids

		train_dataset = NumpyDataset(X, y[:,:n_train], w[:,:n_train], ids)
		valid_dataset = NumpyDataset(
		X, y[:,n_train:n_train+n_valid], w[:,n_train:n_train+n_valid], ids)
		test_dataset = NumpyDataset(
		X, y[:,n_train+n_valid:], w[:,n_train+n_valid:], ids)
		########################################### DEBUG
		print("train_dataset.get_task_names()")
		print(train_dataset.get_task_names())
		print("valid_dataset.get_task_names()")
		print(valid_dataset.get_task_names())
		print("test_dataset.get_task_names()")
		print(test_dataset.get_task_names())
		########################################### DEBUG
		return train_dataset, valid_dataset, test_dataset

deepchem/splits/tests/test_task_splitter.py

0 → 100644

+47 −0

Original line number	Diff line number	Diff line

		"""
		Tests for splitter objects.
		"""
		from __future__ import division
		from __future__ import print_function
		from __future__ import unicode_literals

		__author__ = "Bharath Ramsundar, Aneesh Pappu"
		__copyright__ = "Copyright 2016, Stanford University"
		__license__ = "GPL"

		import tempfile
		import numpy as np
		from deepchem.splits.task_splitter import TaskSplitter
		from deepchem.datasets import NumpyDataset
		from deepchem.datasets.tests import TestDatasetAPI


		class TestTaskSplitters(TestDatasetAPI):
		"""
		Test some basic splitters.
		"""

		def test_multitask_train_valid_test_split(self):
		"""
		Test TaskSplitter train/valid/test split on multitask dataset.
		"""
		n_samples = 100
		n_features = 10
		n_tasks = 10
		X = np.random.rand(n_samples, n_features)
		p = .05 # proportion actives
		y = np.random.binomial(1, p, size=(n_samples, n_tasks))
		dataset = NumpyDataset(X, y)
		########################################### DEBUG
		print("dataset")
		print(dataset)
		########################################### DEBUG

		task_splitter = TaskSplitter()
		train, valid, test = task_splitter.train_valid_test_split(
		dataset, frac_train=.4, frac_valid=.3, frac_test=.3)

		assert len(train.get_task_names()) == 4
		assert len(valid.get_task_names()) == 3
		assert len(test.get_task_names()) == 3

Admin message