Fix X,y reading for all transformer classes (893a48e9) · Commits · 钟慕尧 / deepchem

deepchem/transformers/init.py

+8 −8

Original line number	Diff line number	Diff line
		@@ -90,12 +90,12 @@ class NormalizationTransformer(Transformer):
		row = df.iloc[i]

		if self.transform_X:
		X = load_from_disk(row['X'])
		X = load_from_disk(row['X-transformed'])
		X = np.nan_to_num((X - self.X_means) / self.X_stds)
		save_to_disk(X, row['X-transformed'])

		if self.transform_y:
		y = load_from_disk(row['y'])
		y = load_from_disk(row['y-transformed'])
		y = np.nan_to_num((y - self.y_means) / self.y_stds)
		save_to_disk(y, row['y-transformed'])

		@@ -125,12 +125,12 @@ class ClippingTransformer(Transformer):
		"""
		row = df.iloc[i]
		if self.transform_X:
		X = load_from_disk(row['X'])
		X = load_from_disk(row['X-transformed'])
		X[X > self.max_val] = self.max_val
		X[X < (-1.0self.max_val)] = -1.0 self.max_val
		save_to_disk(X, row['X-transformed'])
		if self.transform_y:
		y = load_from_disk(row['y'])
		y = load_from_disk(row['y-transformed'])
		y[y > trunc] = trunc
		y[y < (-1.0trunc)] = -1.0 trunc
		save_to_disk(y, row['y-transformed'])
		@@ -145,12 +145,12 @@ class LogTransformer(Transformer):
		"""Logarithmically transforms data in dataset."""
		row = df.iloc[i]
		if self.transform_X:
		X = load_from_disk(row['X'])
		X = load_from_disk(row['X-transformed'])
		X = np.log(X)
		save_to_disk(X, row['X-transformed'])

		if self.transform_y:
		y = load_from_disk(row['y'])
		y = load_from_disk(row['y-transformed'])
		y = np.log(y)
		save_to_disk(y, row['y-transformed'])

		@@ -213,7 +213,7 @@ class CoulombRandomizationTransformer(Transformer):
		"""
		row = df.iloc[i]
		if self.transform_X:
		X = load_from_disk(row['X'])
		X = load_from_disk(row['X-transformed'])
		for j in xrange(len(X)):
		cm = self.construct_cm_from_triu(X[j])
		X[j] = self.unpad_randomize_and_flatten(cm)
		@@ -244,7 +244,7 @@ class CoulombBinarizationTransformer(Transformer):
		row = df.iloc[i]
		X_bin = []
		if self.transform_X:
		X = load_from_disk(row['X'])
		X = load_from_disk(row['X-transformed'])
		d = X[0].shape[0]
		for i in xrange(len(X)):
		for j in np.arange(0,self.max+self.theta,self.theta):

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