def test_NewFromFeatureSet(self):
"""Fisher score calculation"""
fs = FeatureSpace.NewFromFitFile(
self.test_fit_path).Normalize(inplace=True)
fw = FisherFeatureWeights.NewFromFeatureSpace(fs)
# test weights generated from test-l.fit:
# wndchrm classify -l -f1.0 -vtest_fit-l.weights test-l.fit test-l.fit
target_fw = FisherFeatureWeights.NewFromFile(
self.test_feat_weight_path)
for target_val, res_val in zip(target_fw.values, fw.values):
self.assertAlmostEqual(target_val, res_val, delta=self.epsilon)
# test slice operator
orig_len = len(fw)
sliced = fw[:10]
self.assertEqual(len(sliced), 10)
self.assertEqual(len(sliced.feature_names), 10)
self.assertEqual(len(sliced.values), 10)
for i in xrange(10):
self.assertEqual(sliced.feature_names[i], fw.feature_names[i])
self.assertEqual(sliced.values[i], fw.values[i])
sliced = fw[50:100:2]
for i, j in zip(range(len(sliced)), range(50, 100, 2)):
self.assertEqual(sliced.feature_names[i], fw.feature_names[j])
self.assertEqual(sliced.values[i], fw.values[j])
def test_NewFromFeatureSet(self):
"""Fisher score calculation"""
feature_set = FeatureSpace.NewFromFitFile(self.test_fit_path)
feature_set.Normalize(inplace=True)
result_weights = FisherFeatureWeights.NewFromFeatureSpace(feature_set)
# test weights generated from test-l.fit:
# wndchrm classify -l -f1.0 -vtest_fit-l.weights test-l.fit test-l.fit
target_weights = FisherFeatureWeights.NewFromFile(
self.test_feat_weight_path)
for target_val, res_val in zip(target_weights.values,
result_weights.values):
self.assertAlmostEqual(target_val, res_val, delta=self.epsilon)
def test_DiscreteTrainTestSplitWithTiling( 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]
#fs = FeatureSet.NewFromFitFile( fitfilepath )
fs = FeatureSpace.NewFromFitFile( fitfilepath, tile_num_rows=5, tile_num_cols=6 )
from numpy.random import RandomState
prng = RandomState(42)
train, test = fs.Split( random_state=prng, quiet=True )
train.Normalize( inplace=True, quiet=True )
fw = FisherFeatureWeights.NewFromFeatureSpace( train ).Threshold()
train.FeatureReduce( fw, inplace=True )
test.FeatureReduce( fw, inplace=True ).Normalize( train, inplace=True, quiet=True )
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)
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_WND5_all_features(self):
epsilon = 0.00001
# Define paths to original files
test_sig_path = join(test_dir, 't1_s01_c05_ij-l_precalculated.sig')
test_fit_path = join(test_dir, 'test-l.fit')
test_feat_wght_path = join(test_dir, 'test_fit-l.weights')
test_tif_path = join(test_dir, 't1_s01_c05_ij.tif')
# Here are the correct values that Python API needs to return:
# wndchrm classify -l -f0.75 test-l.fit t1_s01_c05_ij.tif
# t1_s01_c05_ij.tif 1.6e-27 0.083 0.917 * 4cell 3.835
# wndchrm classify -l test-l.fit t1_s01_c05_ij.tif
# t1_s01_c05_ij.tif 3.19e-27 0.076 0.924 * 4cell 3.848
# wndchrm classify -l -f0.05 test-l.fit t1_s01_c05_ij.tif
# t1_s01_c05_ij.tif 1.06e-26 0.066 0.934 * 4cell 3.869
correct_marg_probs = {}
correct_marg_probs[2189] = [0.083, 0.917]
correct_marg_probs[438] = [0.076, 0.924]
correct_marg_probs[146] = [0.066, 0.934]
# Load the original files once and only once for all this class's tests
feature_set = FeatureSpace.NewFromFitFile(test_fit_path)
fs1 = feature_set.feature_names
feature_set.Normalize()
fs2 = feature_set.feature_names
self.assertSequenceEqual(fs1, fs2)
test_sample = FeatureVector(source_filepath=test_tif_path, long=True)
test_sample.LoadSigFile(test_sig_path)
self.assertSequenceEqual(feature_set.feature_names,
test_sample.feature_names)
test_sample.Normalize(feature_set)
all_weights = FisherFeatureWeights.NewFromFile(test_feat_wght_path)
def Check(num_feats):
weights = all_weights.Threshold(num_feats)
feat_set = feature_set.FeatureReduce(weights)
sample = test_sample.FeatureReduce(weights)
result = SingleSampleClassification.NewWND5(
feat_set, weights, sample)
result_marg_probs = [ round( val, 3 ) \
for val in result.marginal_probabilities ]
for target_val, res_val in zip(correct_marg_probs[num_feats],
result_marg_probs):
self.assertAlmostEqual(target_val, res_val, delta=epsilon)
for num_feats in correct_marg_probs:
Check(num_feats)
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
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)
train_set.ToFitFile()
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()
sys.exit(0)
if from_scratch:
# I preprocessed your training set and feature weights and pickled them for speed.
# Pickle files are binary files that are super fast to load.
# You don't need to use a pickle file though, you can make one from scratch
# Here's how:
# 1. Load the raw c-charm fit file
full_training_set = FeatureSet_Discrete.NewFromFitFile(input_filename)
# 3. Normalize the features:
full_training_set.Normalize()
# 4. Make Fisher scores based on the normalized training set
full_fisher_weights = FisherFeatureWeights.NewFromFeatureSet(
full_training_set)
# 5. Take only the top 200 features
reduced_fisher_weights = full_fisher_weights.Threshold(num_features)
# 6. Reduce the training set feature space to contain only those top 200 features
reduced_training_set = full_training_set.FeatureReduce(
reduced_fisher_weights.names)
# 7. Save your work:
reduced_training_set.PickleMe(
os.path.splitext(input_filename)[0] + ".fit.pickled")
reduced_fisher_weights.PickleMe(
os.path.splitext(input_filename)[0] + "_w" + str(num_features) +
".weights.pickled")
else:
