def test_LDATransform( self ): """LDA transform""" tempdir = mkdtemp() import zipfile zf = zipfile.ZipFile( pychrm_test_dir + sep + 'lymphoma_iicbu2008_subset_EOSIN_ONLY_t5x6_v3.2features.fit.zip', mode='r') zf.extractall( tempdir ) fitfile_path = tempdir + sep + 'lymphoma_iicbu2008_subset_eosin_t5x6_v3.2features.fit' try: kwargs = {} kwargs['pathname'] = fitfile_path kwargs['quiet'] = True # sampling opts: -l -t5x6 implies 5 columns and 6 rows ... I know it's weird. kwargs['long'] = True kwargs['tile_num_rows'] = 6 kwargs['tile_num_cols'] = 5 # against self: fs = FeatureSpace.NewFromFitFile( **kwargs ) self_transformed = fs.LDATransform( reference_features=None, inplace=False ) fit_on_fit_LDA_result = FeatureSpaceClassification.NewWND5( self_transformed, self_transformed, feature_weights=None ) # against other: train, test = fs.Split() train.LDATransform( reference_features=None, inplace=True ) test.LDATransform( reference_features=train, inplace=True ) split_LDA_result = FeatureSpaceClassification.NewWND5( train, test, feature_weights=None ) finally: rmtree( tempdir )
def test_FitOnFitClassification( self ): fitfile_path = wndchrm_test_dir + sep + 'test-l.fit' #fs = FeatureSet.NewFromFitFile( fitfile_path ) fs = FeatureSpace.NewFromFitFile( fitfile_path ) fs.Normalize( inplace=True, quiet=True ) fw = FisherFeatureWeights.NewFromFeatureSpace( fs ).Threshold(438) fw.Print(50) fs.FeatureReduce( fw, inplace=True ) pychrm_split = FeatureSpaceClassification.NewWND5( fs, fs, fw, quiet=False ) pychrm_split = FeatureSpaceClassification.NewWND5_OLD( fs, fs, fw, quiet=False ) from wndcharm.FeatureSpacePredictionExperiment import FeatureSpaceClassificationExperiment html_path = pychrm_test_dir + sep + 'test-l_training_error_result.html' html_exp = FeatureSpaceClassificationExperiment.NewFromHTMLReport( html_path, quiet=False ) # single split in this html html_split = html_exp.individual_results[0] for i, (html_result, pychrm_result) in enumerate( zip( html_split.individual_results,\ pychrm_split.individual_results ) ): try: self.assertEqual( html_result, pychrm_result ) except: outstr = "Error in comparison # {0}:\n".format( i ) outstr += "HTML result:\n{0}\n Python API res:\n{1}".format( html_result, pychrm_result) raise
def test_DiscreteTrainTestSplitNoTiling( self ): """Uses binucleate test set""" fitfilepath = wndchrm_test_dir + sep + 'test-l.fit' fs = FeatureSpace.NewFromFitFile( fitfilepath ) from numpy.random import RandomState prng = RandomState(42) full_train, full_test = fs.Split( random_state=prng, quiet=True ) full_train.Normalize( quiet=True ) reduced_fw = FisherFeatureWeights.NewFromFeatureSpace( full_train ).Threshold() reduced_train = full_train.FeatureReduce( reduced_fw ) reduced_test = full_test.FeatureReduce( reduced_fw ) reduced_test.Normalize( reduced_train, quiet=True ) batch_result = FeatureSpaceClassification.NewWND5( reduced_train, reduced_test, reduced_fw, quiet=True )
def test_IfNotInterpolatable( self ): """You can't graph predicted values if the classes aren't interpolatable.""" testfilename = 'ShouldntBeGraphable.png' small_fs = CreateArtificialFeatureSpace_Discrete( n_samples=20, n_classes=2, random_state=42, interpolatable=False ) train_set, test_set = small_fs.Split( random_state=False, quiet=True ) train_set.Normalize() fw = FisherFeatureWeights.NewFromFeatureSpace( train_set ).Threshold() reduced_train_set = train_set.FeatureReduce( fw ) reduced_test_set = test_set.FeatureReduce( fw ) test_set.Normalize( train_set, quiet=True ) batch_result = FeatureSpaceClassification.NewWND5( reduced_train_set, reduced_test_set, fw, quiet=True ) with self.assertRaises( ValueError ): graph = PredictedValuesGraph( batch_result )
def test_TiledTrainTestSplit(self): """Uses a fake FeatureSpace""" from wndcharm.ArtificialFeatureSpace import CreateArtificialFeatureSpace_Discrete fs_kwargs = {} fs_kwargs['name'] = "DiscreteArtificialFS 10-class" fs_kwargs['n_samples'] = 1000 fs_kwargs['n_classes'] = 10 # 100 samples per class fs_kwargs['num_features_per_signal_type'] = 25 fs_kwargs['initial_noise_sigma'] = 40 fs_kwargs['noise_gradient'] = 20 fs_kwargs['n_samples_per_group'] = 4 # 25 images, 2x2 tiling scheme fs_kwargs['interpolatable'] = True fs_kwargs['random_state'] = 43 fs_kwargs['singularity'] = False fs_kwargs['clip'] = False fs = CreateArtificialFeatureSpace_Discrete(**fs_kwargs) train, test = fs.Split(random_state=False, quiet=True) train.Normalize(inplace=True, quiet=True) fw = FisherFeatureWeights.NewFromFeatureSpace(train).Threshold() train.FeatureReduce(fw, inplace=True) test.FeatureReduce(fw, inplace=True, quiet=True).Normalize(train, inplace=True, quiet=True) result = FeatureSpaceClassification.NewWND5(train, test, fw) result.Print() for class_name in result.test_set.class_names: try: self.assertEqual( result.similarity_matrix[class_name][class_name], float(1)) except: print "offending class: {0}, val: {1}".format( class_name, result.similarity_matrix[class_name][class_name]) raise
def __init__( self, training_set, feature_weights, test_image_path, chart_title=None, max_num_features=300 ): self.timing_axes = None import time timings = [] from wndcharm.FeatureSpacePredictionExperiment import FeatureSpaceClassificationExperiment from wndcharm.SingleSamplePrediction import SingleSampleClassification from wndcharm.FeatureSpacePrediction import FeatureSpaceClassification experiment = FeatureSpaceClassificationExperiment( training_set, training_set, feature_weights ) for number_of_features_to_use in range( 1, max_num_features + 1 ): reduced_ts = None reduced_fw = None three_timings = [] # Take the best of 3 for timing in range( 3 ): # Time the creation and classification of a single signature t1 = time.time() reduced_fw = feature_weights.Threshold( number_of_features_to_use ) sig = FeatureVector( source_filepath=test_image_path, feature_names=reduced_fw.feature_names ).GenerateFeatures() reduced_ts = training_set.FeatureReduce( reduced_fw ) sig.Normalize( reduced_ts ) result = SingleSampleClassification.NewWND5( reduced_ts, reduced_fw, sig ) result.Print() # FIXME: save intermediates just in case of interruption or parallization # result.PickleMe() t2 = time.time() three_timings.append( t2 - t1 ) timings.append( min( three_timings ) ) # now, do a fit-on-fit test to measure classification accuracy split_result = FeatureSpaceClassification.NewWND5( reduced_ts, reduced_ts, reduced_fw ) split_result.Print() experiment.individual_results.append( split_result ) import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt x_vals = list( range( 1, max_num_features + 1 ) ) self.figure = plt.figure() self.main_axes = self.figure.add_subplot(111) if chart_title == None: self.chart_title = "Feature timing v. classification accuracy" else: self.chart_title = chart_title self.main_axes.set_title( self.chart_title ) self.main_axes.set_xlabel( 'Number of features' ) self.main_axes.set_ylabel( 'Classification accuracy (%)', color='b' ) classification_accuracies = \ [ split_result.classification_accuracy * 100 for split_result in experiment.individual_results ] self.main_axes.plot( x_vals, classification_accuracies, color='b', linewidth=2 ) for tl in self.main_axes.get_yticklabels(): tl.set_color('b') self.timing_axes = self.main_axes.twinx() self.timing_axes.set_ylabel( 'Time to calculate features (s)', color='r' ) self.timing_axes.plot( x_vals, timings, color='r' ) for tl in self.timing_axes.get_yticklabels(): tl.set_color('r')
class TestGraphs(unittest.TestCase): """Test WND-CHARM's graph-making functionality.""" fs_kwargs = {} fs_kwargs['name'] = "DiscreteArtificialFS 10-class" fs_kwargs['n_samples'] = 1000 fs_kwargs['n_classes'] = 10 fs_kwargs['num_features_per_signal_type'] = 25 fs_kwargs['initial_noise_sigma'] = 40 fs_kwargs['noise_gradient'] = 20 fs_kwargs['n_samples_per_group'] = 1 fs_kwargs['interpolatable'] = True fs_kwargs['random_state'] = 43 fs_kwargs['singularity'] = False fs_kwargs['clip'] = False fs = CreateArtificialFeatureSpace_Discrete(**fs_kwargs) train_set, test_set = fs.Split(random_state=False, quiet=True) train_set.Normalize(quiet=True) fw = FisherFeatureWeights.NewFromFeatureSpace(train_set).Threshold() reduced_train_set = train_set.FeatureReduce(fw) reduced_test_set = test_set.FeatureReduce(fw) reduced_test_set.Normalize(reduced_train_set, quiet=True) batch_result = FeatureSpaceClassification.NewWND5(reduced_train_set, reduced_test_set, fw, quiet=True) def setUp(self): self.tempdir = mkdtemp() def tearDown(self): rmtree(self.tempdir) def CompareGraphs(self, graph, testfilename): """Helper function to check output graphs""" # Uncoment to see what graph looks like! #graph.SaveToFile( testfilename + 'GRAPH.png' ) # We used to output the graphs to a png file and do a binary diff on a reference png # but there are superficial differences between matplotlib versions that result in # the points still being in the right place, but the font is slightly larger, # or the text is subtlely offset. So now, we interrogate the matplotlib.figure # object and retrieve its coordinates and check them against blessed numpy arrays # saved to a npy file. axessubplot = graph.figure.gca() if len(axessubplot.lines) > 0: # line plot try: all_coords = np.dstack( tuple([ group._path._vertices for group in axessubplot.lines ])) except AttributeError: # older version of matplotlib didn't include leading underscore in attribute # "_vertices" all_coords = np.dstack( tuple( [group._path.vertices for group in axessubplot.lines])) elif len(axessubplot.collections) > 0: # scatter plot all_coords = np.dstack( tuple([group._offsets for group in axessubplot.collections])) else: self.fail("Graph doesn't have any lines nor points") # uncomment to replace old coords #np.save( testfilename, all_coords ) #from os.path import splitext #testfilename_base, ext = splitext( testfilename ) #np.save( testfilename_base + 'NEW.npy', all_coords ) reference_array = np.load(testfilename) if not np.array_equal(all_coords, reference_array): if not np.allclose(all_coords, reference_array): errmsg = 'Reference graph "{0}" coordinates '.format(testfilename) + \ 'do not concur with coordinates generated by this test.' self.fail(errmsg) @unittest.skipIf(HasMatplotlib, "Skipped if matplotlib IS installed") def test_ErrMsgIfMatplotibNotInstalled(self): """Fail gracefully with informative message if matplotlib""" graph = PredictedValuesGraph(self.batch_result) with self.assertRaises(ImportError): graph.RankOrderedPredictedValuesGraph() with self.assertRaises(ImportError): graph.KernelSmoothedDensityGraph() @unittest.skipUnless(HasMatplotlib, "Skipped if matplotlib IS NOT installed") @unittest.expectedFailure def test_RankOrderedFromBatchClassificationResult(self): """Rank Ordered Predicted values graph from a single split""" testfilename = 'test_graph_rank_ordered_interpolated_discrete.npy' graph = PredictedValuesGraph(self.batch_result) graph.RankOrderedPredictedValuesGraph() self.CompareGraphs(graph, testfilename) @unittest.skipUnless(HasMatplotlib, "Skipped if matplotlib IS NOT installed") @unittest.expectedFailure def test_KernelSmoothedFromBatchClassificationResult(self): """Kernel Smoothed Probability density graph from a single split""" testfilename = 'test_graph_kernel_smoothed.npy' graph = PredictedValuesGraph(self.batch_result) graph.KernelSmoothedDensityGraph() self.CompareGraphs(graph, testfilename) @unittest.skipUnless(HasMatplotlib, "Skipped if matplotlib IS NOT installed") def test_FromDiscreteClassificationExperimentResults(self): """Rank Ordered Predicted values graph from an experiment result (multiple splits)""" testfilename = 'test_graph_rank_ordered_experiment.npy' # Make a smaller featureset to do multiple splits fs_kwargs = {} fs_kwargs['name'] = "DiscreteArtificialFS RANK ORDERED SHUFFLE SPLIT" fs_kwargs['n_samples'] = 100 # smaller fs_kwargs['n_classes'] = 5 # smaller, 20 samples per class fs_kwargs['num_features_per_signal_type'] = 10 # smaller fs_kwargs['initial_noise_sigma'] = 50 fs_kwargs['noise_gradient'] = 20 fs_kwargs['n_samples_per_group'] = 1 fs_kwargs['interpolatable'] = True fs_kwargs['random_state'] = 42 fs_kwargs['singularity'] = False fs_kwargs['clip'] = False small_fs = CreateArtificialFeatureSpace_Discrete(**fs_kwargs) ss_kwargs = {} ss_kwargs['quiet'] = True ss_kwargs['n_iter'] = n_iter = 10 ss_kwargs['train_size'] = train_size = 18 # per-class ss_kwargs['test_size'] = test_size = 2 # per-class ss_kwargs['random_state'] = 42 exp = FeatureSpaceClassificationExperiment.NewShuffleSplit( small_fs, **ss_kwargs) graph = PredictedValuesGraph(exp, use_averaged_results=False) graph.RankOrderedPredictedValuesGraph() self.CompareGraphs(graph, testfilename) @unittest.skipUnless(HasMatplotlib, "Skipped if matplotlib IS NOT installed") def test_HyperparameterOptimizationGraph(self): """Accuracy vs. # features or samples with and without LDA feature space transform""" testfilename = 'test_graph_rank_ordered_experiment.npy' # Make a smaller featureset to do multiple splits fs_kwargs = {} fs_kwargs['name'] = "DiscreteArtificialFS RANK ORDERED SHUFFLE SPLIT" fs_kwargs['n_samples'] = 100 # smaller fs_kwargs['n_classes'] = 5 # smaller, 20 samples per class fs_kwargs['num_features_per_signal_type'] = 10 # smaller fs_kwargs['initial_noise_sigma'] = 50 fs_kwargs['noise_gradient'] = 20 fs_kwargs['n_samples_per_group'] = 1 fs_kwargs['interpolatable'] = True fs_kwargs['random_state'] = 42 fs_kwargs['singularity'] = False fs_kwargs['clip'] = False small_fs = CreateArtificialFeatureSpace_Discrete(**fs_kwargs) ss_kwargs = {} ss_kwargs['quiet'] = False ss_kwargs['n_iter'] = n_iter = 10 ss_kwargs['train_size'] = train_size = 18 # per-class ss_kwargs['test_size'] = test_size = 2 # per-class ss_kwargs['random_state'] = 42 ss_kwargs['show_raw'] = True ss_kwargs['show_lda'] = True ss_kwargs['param'] = 'features' ss_kwargs['text_angle'] = -30 graph = HyperparameterOptimizationGraph(small_fs) graph.GridSearch(**ss_kwargs) #graph.savefig( '/Users/colettace/test_features.png' ) ss_kwargs['param'] = 'samples' ss_kwargs['quiet'] = False ss_kwargs['text_angle'] = -30 graph = HyperparameterOptimizationGraph(small_fs) graph.GridSearch(**ss_kwargs) #graph.savefig( '/Users/colettace/test_samples.png' ) @unittest.skipUnless(HasMatplotlib, "Skipped if matplotlib IS NOT installed") def test_FromHTML(self): """Rank Ordered Predicted values graph from an experiment result (multiple splits)""" testfilename = 'test_graph_fromHTML.npy' # Inflate the zipped html file into a temp file import zipfile #zipped_file_path = pychrm_test_dir + sep + 'c_elegans_terminal_bulb.html' #import zlib #zf = zipfile.ZipFile( zipped_file_path + '.zip', mode='w' ) #zf.write( zipped_file_path, compress_type=zipfile.ZIP_DEFLATED ) #zf.close() zipped_file_path = pychrm_test_dir + sep + 'c_elegans_terminal_bulb.html.zip' zf = zipfile.ZipFile(zipped_file_path, mode='r') zf.extractall(self.tempdir) htmlfilepath = self.tempdir + sep + zf.namelist()[0] graph = PredictedValuesGraph.NewFromHTMLReport( htmlfilepath, use_averaged_results=False) graph.RankOrderedPredictedValuesGraph() self.CompareGraphs(graph, testfilename) @unittest.skipUnless(HasMatplotlib, "Skipped if matplotlib IS NOTinstalled") def test_IfNotInterpolatable(self): """You can't graph predicted values if the classes aren't interpolatable.""" testfilename = 'ShouldntBeGraphable.png' small_fs = CreateArtificialFeatureSpace_Discrete(n_samples=20, n_classes=2, random_state=42, interpolatable=False) train_set, test_set = small_fs.Split(random_state=False, quiet=True) train_set.Normalize() fw = FisherFeatureWeights.NewFromFeatureSpace(train_set).Threshold() reduced_train_set = train_set.FeatureReduce(fw) reduced_test_set = test_set.FeatureReduce(fw) test_set.Normalize(train_set, quiet=True) batch_result = FeatureSpaceClassification.NewWND5(reduced_train_set, reduced_test_set, fw, quiet=True) with self.assertRaises(ValueError): graph = PredictedValuesGraph(batch_result)
def test_FitOnFit(self): """Uses a curated subset of the IICBU 2008 Lymphoma dataset, preprocessed as follows: auto-deconvolved, eosin channel only, tiled 5x6, 3 classes, 10 imgs per class, 300 samples per class. """ # Inflate the zipped test fit into a temp file import zipfile zipped_file_path = pychrm_test_dir + sep + 'lymphoma_iicbu2008_subset_EOSIN_ONLY_t5x6_v3.2features.fit.zip' zf = zipfile.ZipFile(zipped_file_path, mode='r') tempdir = mkdtemp() zf.extractall(tempdir) try: fitfilepath = tempdir + sep + zf.namelist()[0] # Do fit on fit WITHOUT tiling and compare with fit on fit results # generated with wndchrm 1.60 fs = FeatureSpace.NewFromFitFile(fitfilepath).Normalize( inplace=True, quiet=True) #fs = FeatureSpace.NewFromFitFile( wndchrm_test_dir + sep + 'test-l.fit' ) #fs.ToFitFile( 'temp.fit' ) fw = FisherFeatureWeights.NewFromFeatureSpace(fs).Threshold() fs.FeatureReduce(fw, inplace=True) # #fw.Print() # #fs.Print(verbose=True) pychrm_res = FeatureSpaceClassification.NewWND5(fs, fs, fw) pychrm_res.Print() # # import cProfile as pr # #import profile as pr # import tempfile # import pstats # prof = tempfile.NamedTemporaryFile() # cmd = 'no_tile_pychrm_result = DiscreteBatchClassificationResult.New( reduced_fs, reduced_fs, fw )' # pr.runctx( cmd, globals(), locals(), prof.name) # p = pstats.Stats(prof.name) # p.sort_stats('time').print_stats(20) # prof.close() self.maxDiff = None html_path = pychrm_test_dir + sep + 'lymphoma_iicbu2008_subset_eosin_t5x6_v3.2feats_REFERENCE_RESULTS_900_samples_TRAINING_ERROR.html' wres = FeatureSpaceClassificationExperiment.NewFromHTMLReport( html_path) wres.Print() wc_batch_result = wres.individual_results[ 0] # only 1 split in fit-on-fit # This takes WAY too long: #self.assertSequenceEqual( wc_batch_result.individual_results, pychrm_res.individual_results ) wc_result = np.empty((3 * len(wc_batch_result.individual_results))) for i, single_result in enumerate( wc_batch_result.individual_results): wc_result[i * 3:(i + 1) * 3] = single_result.marginal_probabilities pc_result = np.empty((3 * len(pychrm_res.individual_results))) for i, single_result in enumerate(pychrm_res.individual_results): # HTML report only has 3 decimal places pc_result[ i*3 : (i+1)*3 ] = \ [ float( "{0:0.3f}".format( val ) ) for val in single_result.marginal_probabilities ] from numpy.testing import assert_allclose assert_allclose(actual=pc_result, desired=wc_result, atol=0.003) #wc_batch_result.Print() #pres.Print() # ========================================================== # Now do the same with tiling, reusing fs from before: num_samples_per_group = 30 n_groups = fs.num_samples / num_samples_per_group new_sg_ids = [ i for i in xrange(n_groups) for j in xrange(num_samples_per_group) ] fs.Update( tile_num_rows=5, tile_num_cols=6, num_samples_per_group=30,\ _contiguous_sample_group_ids=new_sg_ids )._RebuildViews() with_tile_pychrm_result = FeatureSpaceClassification.NewWND5( fs, fs, fw) html_path = pychrm_test_dir + sep + 'lymphoma_iicbu2008_subset_eosin_t5x6_v3.2feats_REFERENCE_RESULTS_30_samples_tiled_TRAINING_ERROR.html' with_tile_wndchrm_result = \ FeatureSpaceClassificationExperiment.NewFromHTMLReport( html_path ).individual_results[0] #self.assertSequenceEqual( with_tile_pychrm_result.averaged_results, with_tile_wndchrm_result.individual_results ) wc_result = np.empty( (3 * len(with_tile_wndchrm_result.individual_results))) for i, single_result in enumerate( with_tile_wndchrm_result.individual_results): wc_result[i * 3:(i + 1) * 3] = single_result.marginal_probabilities pc_result = np.empty( (3 * len(with_tile_pychrm_result.averaged_results))) for i, single_result in enumerate( with_tile_pychrm_result.averaged_results): # HTML report only has 3 decimal places pc_result[ i*3 : (i+1)*3 ] = \ [ float( "{0:0.3f}".format( val ) ) for val in single_result.marginal_probabilities ] assert_allclose(actual=pc_result, desired=wc_result, atol=0.003) finally: rmtree(tempdir)
if feature_usage_fraction: if feature_usage_fraction < 0 or feature_usage_fraction > 1.0: raise Exception('Feature usage fraction must be on interval [0,1]') num_features = int( feature_usage_fraction * train_set.num_features ) if num_features: print "Using top {0} Fisher-ranked features.".format( num_features ) else: print "Using top 15% Fisher-ranked features." experiment = FeatureSpaceClassificationExperiment( training_set=train_set ) train_set.Normalize( inplace=True ) weights = FisherFeatureWeights.NewFromFeatureSpace( train_set ).Threshold( num_features ) train_set.FeatureReduce( weights, inplace=True ) if train_set != test_set: test_set.FeatureReduce( weights, inplace=True ).Normalize( train_set ) for i in range( num_splits ): split = FeatureSpaceClassification.NewWND5( train_set, test_set, weights, batch_number=i ) experiment.individual_results.append( split ) if outpath: experiment.Print( output_filepath=outpath, mode='w' ) #experiment.PerSampleStatistics( output_filepath=outpath, mode= 'a' ) else: experiment.Print() #experiment.PerSampleStatistics()