Add documentation to SingletaskStratifiedSplitter (cc222811) · Commits · 钟慕尧 / deepchem

deepchem/splits/splitters.py

+26 −5

Original line number	Diff line number	Diff line
		@@ -277,7 +277,8 @@ class SingletaskStratifiedSplitter(Splitter):
		"""
		Creates splitter object.

		Parameters:
		Parameters
		----------
		task_number: int (Optional, Default 0)
		Task number for stratification.
		verbose: bool (Optional, Default False)
		@@ -300,7 +301,7 @@ class SingletaskStratifiedSplitter(Splitter):
		seed: int (Optional, Default None)
		Random seed.
		log_every_n: int (Optional, Default None)
		Log every n examples (not currently used)
		Log every n examples (not currently used).

		Returns
		-------
		@@ -329,6 +330,26 @@ class SingletaskStratifiedSplitter(Splitter):
		frac_test=.1, log_every_n=None):
		"""
		Splits compounds into train/validation/test using stratified sampling.

		Parameters
		----------
		dataset: dc.data.Dataset object
		Dataset.
		seed: int (Optional, Default None)
		Random seed.
		frac_train: float (Optional, Default .8)
		Fraction of dataset put into training data.
		frac_valid: float (Optional, Default .1)
		Fraction of dataset put into validation data.
		frac_test: float (Optional, Default .1)
		Fraction of dataset put into test data.
		log_every_n: int (Optional, Default None)
		Log every n examples (not currently used).

		Returns
		-------
		retval: Tuple
		Tuple containing train indices, valid indices, and test indices
		"""
		# JSG Assert that split fractions can be written as proper fractions over 10.
		# This can be generalized in the future with some common demoninator determination.
		@@ -339,13 +360,13 @@ class SingletaskStratifiedSplitter(Splitter):
		if not seed is None:
		np.random.seed(seed)

		y_s = dataset.y[self.task_number]
		y_s = dataset.y[:,self.task_number]
		sortidx = np.argsort(y_s)

		split_cd = 10
		train_cutoff = int(frac_train * split_cd)
		valid_cutoff = int(frac_valid * split_cd)
		test_cutoff = int(frac_test * split_cd)
		valid_cutoff = int(frac_valid * split_cd) + train_cutoff
		test_cutoff = int(frac_test * split_cd) + valid_cutoff

		train_idx = np.array([])
		valid_idx = np.array([])

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