Fixes to transformers (2aaf2dac) · Commits · 钟慕尧 / deepchem

deepchem/transformers/init.py

+0 −1

Original line number	Diff line number	Diff line
		@@ -5,7 +5,6 @@ from __future__ import print_function
		from __future__ import division
		from __future__ import unicode_literals

		# TODO(rbharath): Get rid of * import
		from deepchem.transformers.transformers import undo_transforms
		from deepchem.transformers.transformers import undo_grad_transforms
		from deepchem.transformers.transformers import LogTransformer

deepchem/transformers/tests/test_transformers.py

+62 −46

Original line number	Diff line number	Diff line
		@@ -9,30 +9,32 @@ __author__ = "Bharath Ramsundar"
		__copyright__ = "Copyright 2016, Stanford University"
		__license__ = "GPL"

		import os
		import unittest
		import numpy as np
		import pandas as pd
		import deepchem as dc
		import numpy.random as random
		import os
		from deepchem.datasets import DiskDataset
		from deepchem.transformers import LogTransformer
		from deepchem.transformers import NormalizationTransformer
		from deepchem.transformers import BalancingTransformer
		from deepchem.transformers import CDFTransformer
		from deepchem.transformers import PowerTransformer
		from deepchem.datasets.tests import TestDatasetAPI

		class TestTransformerAPI(TestDatasetAPI):
		"""Test top-level API for transformer objects."""
		class TestTransformers(unittest.TestCase):
		"""
		Test top-level API for transformer objects.
		"""
		def setUp(self):
		super(TestTransformers, self).setUp()
		self.current_dir = os.path.dirname(os.path.abspath(__file__))


		def test_y_log_transformer(self):
		"""Tests logarithmic data transformer."""
		solubility_dataset = self.load_solubility_data()
		log_transformer = LogTransformer(
		solubility_dataset = dc.datasets.tests.load_solubility_data()
		log_transformer = dc.transformers.LogTransformer(
		transform_y=True, dataset=solubility_dataset)
		X, y, w, ids = (solubility_dataset.X, solubility_dataset.y, solubility_dataset.w, solubility_dataset.ids)
		X, y, w, ids = (solubility_dataset.X, solubility_dataset.y,
		solubility_dataset.w, solubility_dataset.ids)
		solubility_dataset = log_transformer.transform(solubility_dataset)
		X_t, y_t, w_t, ids_t = (solubility_dataset.X, solubility_dataset.y, solubility_dataset.w, solubility_dataset.ids)
		X_t, y_t, w_t, ids_t = (solubility_dataset.X, solubility_dataset.y,
		solubility_dataset.w, solubility_dataset.ids)

		# Check ids are unchanged.
		for id_elt, id_t_elt in zip(ids, ids_t):
		@@ -49,12 +51,14 @@ class TestTransformerAPI(TestDatasetAPI):

		def test_X_log_transformer(self):
		"""Tests logarithmic data transformer."""
		solubility_dataset = self.load_solubility_data()
		log_transformer = LogTransformer(
		solubility_dataset = dc.datasets.tests.load_solubility_data()
		log_transformer = dc.transformers.LogTransformer(
		transform_X=True, dataset=solubility_dataset)
		X, y, w, ids = (solubility_dataset.X, solubility_dataset.y, solubility_dataset.w, solubility_dataset.ids)
		X, y, w, ids = (solubility_dataset.X, solubility_dataset.y,
		solubility_dataset.w, solubility_dataset.ids)
		solubility_dataset = log_transformer.transform(solubility_dataset)
		X_t, y_t, w_t, ids_t = (solubility_dataset.X, solubility_dataset.y, solubility_dataset.w, solubility_dataset.ids)
		X_t, y_t, w_t, ids_t = (solubility_dataset.X, solubility_dataset.y,
		solubility_dataset.w, solubility_dataset.ids)

		# Check ids are unchanged.
		for id_elt, id_t_elt in zip(ids, ids_t):
		@@ -71,7 +75,7 @@ class TestTransformerAPI(TestDatasetAPI):

		def test_y_log_transformer_select(self):
		"""Tests logarithmic data transformer with selection."""
		multitask_dataset = self.load_feat_multitask_data()
		multitask_dataset = dc.datasets.tests.load_feat_multitask_data()
		dfe = pd.read_csv(os.path.join(self.current_dir,
		"../../models/tests/feat_multitask_example.csv"))
		tid = []
		@@ -81,7 +85,7 @@ class TestTransformerAPI(TestDatasetAPI):
		tiid = dfe.columns.get_loc(task)-dfe.columns.get_loc(first_task)
		tid = np.concatenate((tid, np.array([tiid])))
		tasks = tid.astype(int)
		log_transformer = LogTransformer(
		log_transformer = dc.transformers.LogTransformer(
		transform_y=True, tasks=tasks,
		dataset=multitask_dataset)
		X, y, w, ids = (multitask_dataset.X, multitask_dataset.y, multitask_dataset.w, multitask_dataset.ids)
		@@ -103,7 +107,7 @@ class TestTransformerAPI(TestDatasetAPI):

		def test_X_log_transformer_select(self):
		#Tests logarithmic data transformer with selection.
		multitask_dataset = self.load_feat_multitask_data()
		multitask_dataset = dc.datasets.tests.load_feat_multitask_data()
		dfe = pd.read_csv(os.path.join(self.current_dir,
		"../../models/tests/feat_multitask_example.csv"))
		fid = []
		@@ -113,7 +117,7 @@ class TestTransformerAPI(TestDatasetAPI):
		fiid = dfe.columns.get_loc(feature)-dfe.columns.get_loc(first_feature)
		fid = np.concatenate((fid, np.array([fiid])))
		features = fid.astype(int)
		log_transformer = LogTransformer(
		log_transformer = dc.transformers.LogTransformer(
		transform_X=True, features=features,
		dataset=multitask_dataset)
		X, y, w, ids = (multitask_dataset.X, multitask_dataset.y, multitask_dataset.w, multitask_dataset.ids)
		@@ -135,12 +139,14 @@ class TestTransformerAPI(TestDatasetAPI):

		def test_y_normalization_transformer(self):
		"""Tests normalization transformer."""
		solubility_dataset = self.load_solubility_data()
		normalization_transformer = NormalizationTransformer(
		solubility_dataset = dc.datasets.tests.load_solubility_data()
		normalization_transformer = dc.transformers.NormalizationTransformer(
		transform_y=True, dataset=solubility_dataset)
		X, y, w, ids = (solubility_dataset.X, solubility_dataset.y, solubility_dataset.w, solubility_dataset.ids)
		X, y, w, ids = (solubility_dataset.X, solubility_dataset.y,
		solubility_dataset.w, solubility_dataset.ids)
		solubility_dataset = normalization_transformer.transform(solubility_dataset)
		X_t, y_t, w_t, ids_t = (solubility_dataset.X, solubility_dataset.y, solubility_dataset.w, solubility_dataset.ids)
		X_t, y_t, w_t, ids_t = (solubility_dataset.X, solubility_dataset.y,
		solubility_dataset.w, solubility_dataset.ids)
		# Check ids are unchanged.
		for id_elt, id_t_elt in zip(ids, ids_t):
		assert id_elt == id_t_elt
		@@ -157,12 +163,14 @@ class TestTransformerAPI(TestDatasetAPI):

		def test_X_normalization_transformer(self):
		"""Tests normalization transformer."""
		solubility_dataset = self.load_solubility_data()
		normalization_transformer = NormalizationTransformer(
		solubility_dataset = dc.datasets.tests.load_solubility_data()
		normalization_transformer = dc.transformers.NormalizationTransformer(
		transform_X=True, dataset=solubility_dataset)
		X, y, w, ids = (solubility_dataset.X, solubility_dataset.y, solubility_dataset.w, solubility_dataset.ids)
		X, y, w, ids = (solubility_dataset.X, solubility_dataset.y,
		solubility_dataset.w, solubility_dataset.ids)
		solubility_dataset = normalization_transformer.transform(solubility_dataset)
		X_t, y_t, w_t, ids_t = (solubility_dataset.X, solubility_dataset.y, solubility_dataset.w, solubility_dataset.ids)
		X_t, y_t, w_t, ids_t = (solubility_dataset.X, solubility_dataset.y,
		solubility_dataset.w, solubility_dataset.ids)
		# Check ids are unchanged.
		for id_elt, id_t_elt in zip(ids, ids_t):
		assert id_elt == id_t_elt
		@@ -190,9 +198,10 @@ class TestTransformerAPI(TestDatasetAPI):
		"""Test CDF transformer on Gaussian normal dataset."""
		target = np.array(np.transpose(np.linspace(0.,1.,1001)))
		target = np.transpose(np.array(np.append([target],[target], axis=0)))
		gaussian_dataset = self.load_gaussian_cdf_data()
		gaussian_dataset = dc.datasets.tests.load_gaussian_cdf_data()
		bins=1001
		cdf_transformer = CDFTransformer(transform_X=True, bins=bins)
		cdf_transformer = dc.transformers.CDFTransformer(transform_X=True,
		bins=bins)
		X, y, w, ids = (gaussian_dataset.X,gaussian_dataset.y,gaussian_dataset.w,gaussian_dataset.ids)
		gaussian_dataset = cdf_transformer.transform(gaussian_dataset, bins=bins)
		X_t, y_t, w_t, ids_t = (gaussian_dataset.X,gaussian_dataset.y,gaussian_dataset.w,gaussian_dataset.ids)
		@@ -213,13 +222,16 @@ class TestTransformerAPI(TestDatasetAPI):
		#Test CDF transformer on Gaussian normal dataset.
		target = np.array(np.transpose(np.linspace(0.,1.,1001)))
		target = np.transpose(np.array(np.append([target],[target], axis=0)))
		gaussian_dataset = self.load_gaussian_cdf_data()
		gaussian_dataset = dc.datasets.tests.load_gaussian_cdf_data()
		bins=1001
		cdf_transformer = CDFTransformer(transform_y=True, bins=bins)
		X, y, w, ids = (gaussian_dataset.X,gaussian_dataset.y,gaussian_dataset.w,gaussian_dataset.ids)
		cdf_transformer = dc.transformers.CDFTransformer(transform_y=True, bins=bins)
		X, y, w, ids = (gaussian_dataset.X, gaussian_dataset.y, gaussian_dataset.w,
		gaussian_dataset.ids)
		gaussian_dataset = cdf_transformer.transform(gaussian_dataset, bins=bins)
		gaussian_dataset = DiskDataset(data_dir=gaussian_dataset.data_dir,reload=True)
		X_t, y_t, w_t, ids_t = (gaussian_dataset.X,gaussian_dataset.y,gaussian_dataset.w,gaussian_dataset.ids)
		gaussian_dataset = dc.datasets.DiskDataset(
		data_dir=gaussian_dataset.data_dir,reload=True)
		X_t, y_t, w_t, ids_t = (gaussian_dataset.X, gaussian_dataset.y,
		gaussian_dataset.w, gaussian_dataset.ids)

		# Check ids are unchanged.
		for id_elt, id_t_elt in zip(ids, ids_t):
		@@ -235,12 +247,14 @@ class TestTransformerAPI(TestDatasetAPI):

		def test_power_X_transformer(self):
		"""Test Power transformer on Gaussian normal dataset."""
		gaussian_dataset = self.load_gaussian_cdf_data()
		gaussian_dataset = dc.datasets.tests.load_gaussian_cdf_data()
		powers=[1,2,0.5]
		power_transformer = PowerTransformer(transform_X=True, powers=powers)
		power_transformer = dc.transformers.PowerTransformer(
		transform_X=True, powers=powers)
		X, y, w, ids = (gaussian_dataset.X,gaussian_dataset.y,gaussian_dataset.w,gaussian_dataset.ids)
		gaussian_dataset = power_transformer.transform(gaussian_dataset)
		gaussian_dataset = DiskDataset(data_dir=gaussian_dataset.data_dir,reload=True)
		gaussian_dataset = dc.datasets.DiskDataset(
		data_dir=gaussian_dataset.data_dir,reload=True)
		X_t, y_t, w_t, ids_t = (gaussian_dataset.X,gaussian_dataset.y,gaussian_dataset.w,gaussian_dataset.ids)

		# Check ids are unchanged.
		@@ -258,8 +272,8 @@ class TestTransformerAPI(TestDatasetAPI):
		def test_singletask_balancing_transformer(self):
		"""Test balancing transformer on single-task dataset."""

		classification_dataset = self.load_classification_data()
		balancing_transformer = BalancingTransformer(
		classification_dataset = dc.datasets.tests.load_classification_data()
		balancing_transformer = dc.transformers.BalancingTransformer(
		transform_w=True, dataset=classification_dataset)
		X, y, w, ids = (classification_dataset.X, classification_dataset.y, classification_dataset.w, classification_dataset.ids)
		classification_dataset = balancing_transformer.transform(classification_dataset)
		@@ -284,12 +298,14 @@ class TestTransformerAPI(TestDatasetAPI):

		def test_multitask_balancing_transformer(self):
		"""Test balancing transformer on multitask dataset."""
		multitask_dataset = self.load_multitask_data()
		balancing_transformer = BalancingTransformer(
		multitask_dataset = dc.datasets.tests.load_multitask_data()
		balancing_transformer = dc.transformers.BalancingTransformer(
		transform_w=True, dataset=multitask_dataset)
		X, y, w, ids = (multitask_dataset.X, multitask_dataset.y, multitask_dataset.w, multitask_dataset.ids)
		X, y, w, ids = (multitask_dataset.X, multitask_dataset.y,
		multitask_dataset.w, multitask_dataset.ids)
		multitask_dataset = balancing_transformer.transform(multitask_dataset)
		X_t, y_t, w_t, ids_t = (multitask_dataset.X, multitask_dataset.y, multitask_dataset.w, multitask_dataset.ids)
		X_t, y_t, w_t, ids_t = (multitask_dataset.X, multitask_dataset.y,
		multitask_dataset.w, multitask_dataset.ids)
		# Check ids are unchanged.
		for id_elt, id_t_elt in zip(ids, ids_t):
		assert id_elt == id_t_elt

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