fixed bugs in tests, but not done (934c1672) · Commits · 钟慕尧 / deepchem

deepchem/splits/init.py

+13 −12

Original line number	Diff line number	Diff line
		@@ -85,12 +85,12 @@ class StratifiedSplitter(Splitter):
		Class for doing stratified splits -- where data is too sparse to do regular splits
		"""

		def __randomizeArrays(self, arraylist):
		generator_state = numpy.random.get_state()
		for array in arrayList:
		numpy.random.shuffle(array)
		numpy.random.set_state(generator_state)
		return arrayList
		def __randomizeArrays(self, array_list):
		generator_state = np.random.get_state()
		for array in array_list:
		np.random.shuffle(array)
		np.random.set_state(generator_state)
		return array_list

		def __generate_required_hits(self, y_df, frac_train):
		colIndex = 0
		@@ -111,7 +111,6 @@ class StratifiedSplitter(Splitter):
		column = y_df[col]
		num_hit = 0
		num_required = required_hit_dict[colIndex]
		colIndex += 1
		for index, value in y_df[col].iteritems():
		if pd.notnull(value):
		num_hit += 1
		@@ -119,6 +118,7 @@ class StratifiedSplitter(Splitter):
		if num_hit >= num_required:
		index_dict[colIndex] = index
		break
		colIndex+=1
		return index_dict

		def train_valid_test_split(self, dataset, train_dir,
		@@ -128,7 +128,7 @@ class StratifiedSplitter(Splitter):
		# Obtain original x, y, and w arrays
		numpyArrayList = dataset.to_numpy();

		numpyArrayList = randomizeArrays(numpyArrayList)
		numpyArrayList = self.__randomizeArrays(numpyArrayList)
		X = numpyArrayList[0]
		y = numpyArrayList[1]
		w = numpyArrayList[2]
		@@ -139,14 +139,15 @@ class StratifiedSplitter(Splitter):
		"""
		# find, for each task, the total number of hits and calculate the required
		# number of hits for valid split based on frac_train
		x_df = pd.DataFrame(data=x)
		x_df = pd.DataFrame(data=X)
		y_df = pd.DataFrame(data=y)
		w_df = pd.DataFrame(data=w)
		id_df = pd.DataFrame(data=ids)

		required_hit_dict = __generate_required_hits(y_df, frac_train)
		index_dict = __generate_required_index(y_df, required_hit_dict)
		X_train, X_test, y_train, y_test, w_train, w_test, id_train, id_test = []
		required_hit_dict = self.__generate_required_hits(y_df, frac_train)
		index_dict = self.__generate_required_index(y_df, required_hit_dict)
		print(index_dict)
		X_train, X_test, y_train, y_test, w_train, w_test, id_train, id_test = [], [], [], [], [], [], [], []

		# cycle through rows in y, copy over rows as appropriate
		for rowIndex, row in y_df.iterrows():

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