def test_mass_univariate_classification_gnb_2d(self):
"""Simple classification problem, 2d features"""
X = array([-1, 1, -2, -1, -3, -2, 1, 1, 2, 1, 3, 2])
features = array([1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2])
samples = array([1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6])
labels = array([1, 1, 1, 2, 2, 2])
params = dict([('labels', labels), ('features', features), ('samples', samples)])
clf = MassUnivariateClassifier.load(params, "gaussnaivebayes", cv=0)
data = self.sc.parallelize(zip([1], [X]))
# first feature predicts perfectly
result = clf.classify(data, [[1]]).map(lambda (_, v): v).collect()
assert_array_almost_equal(result[0], [1.0])
# second feature gets one wrong
result = clf.classify(data, [[2]]).map(lambda (_, v): v).collect()
assert_array_almost_equal(result[0], [5.0/6.0])
# two features together predict perfectly
result = clf.classify(data, [[1, 2]]).map(lambda (_, v): v).collect()
assert_array_almost_equal(result[0], [1.0])
# test iteration over multiple feature sets
result = clf.classify(data, [[1, 2], [2]]).map(lambda (_, v): v).collect()
assert_array_almost_equal(result[0], [1.0, 5.0/6.0])
def test_mass_univariate_classification_gnb_2d(self):
"""Simple classification problem, 2d features"""
X = array([-1, 1, -2, -1, -3, -2, 1, 1, 2, 1, 3, 2])
features = array([1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2])
samples = array([1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6])
labels = array([1, 1, 1, 2, 2, 2])
params = dict([('labels', labels), ('features', features),
('samples', samples)])
clf = MassUnivariateClassifier.load(params, "gaussnaivebayes", cv=0)
data = self.sc.parallelize(zip([1], [X]))
# first feature predicts perfectly
result = clf.classify(data, [[1]]).map(lambda (_, v): v).collect()
assert_array_almost_equal(result[0], [1.0])
# second feature gets one wrong
result = clf.classify(data, [[2]]).map(lambda (_, v): v).collect()
assert_array_almost_equal(result[0], [5.0 / 6.0])
# two features together predict perfectly
result = clf.classify(data, [[1, 2]]).map(lambda (_, v): v).collect()
assert_array_almost_equal(result[0], [1.0])
# test iteration over multiple feature sets
result = clf.classify(data,
[[1, 2], [2]]).map(lambda (_, v): v).collect()
assert_array_almost_equal(result[0], [1.0, 5.0 / 6.0])
def test_mass_univariate_classification_ttest_2d(self):
"""Simple classification problem, 2d features"""
X = array([-1, -2, -0.1, -2, -0.1, -2.1, 1, 1.1, 1, 1, 1.1, 2])
features = array([1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2])
samples = array([1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6])
labels = array([1, 1, 1, 2, 2, 2])
params = dict([('labels', labels), ('features', features),
('samples', samples)])
clf = MassUnivariateClassifier.load(params, "ttest")
# should match direct calculation using scipy
# test first feature only
data = self.sc.parallelize(zip([1], [X]))
result = clf.classify(data, [[1]]).map(lambda (_, v): v).collect()
ground_truth = ttest_ind(X[features == 1][:3], X[features == 1][3:])
assert_array_almost_equal(result[0], ground_truth[0])
# test both features
result = clf.classify(data, [[1, 2]]).map(lambda (_, v): v).collect()
ground_truth = ttest_ind(
vstack((X[features == 1][:3], X[features == 2][:3])).T,
vstack((X[features == 1][3:], X[features == 2][3:])).T)
assert_array_almost_equal(result[0][0], ground_truth[0])
def test_mass_univariate_classification_ttest_1d(self):
"""Simple classification problem, 1d features"""
X = array([-1, -0.1, -0.1, 1, 1, 1.1])
labels = array([1, 1, 1, 2, 2, 2])
params = dict([('labels', labels)])
clf = MassUnivariateClassifier.load(params, "ttest")
# should match direct calculation using scipy
data = self.sc.parallelize(zip([1], [X]))
result = clf.classify(data).map(lambda (_, v): v).collect()
ground_truth = ttest_ind(X[labels == 1], X[labels == 2])
assert_array_almost_equal(result[0], ground_truth[0])
def test_mass_univariate_classification_ttest_1d(self):
"""Simple classification problem, 1d features"""
X = array([-1, -0.1, -0.1, 1, 1, 1.1])
labels = array([1, 1, 1, 2, 2, 2])
params = dict([('labels', labels)])
clf = MassUnivariateClassifier.load(params, "ttest")
# should match direct calculation using scipy
data = self.sc.parallelize(zip([1], [X]))
result = clf.classify(data).map(lambda (_, v): v).collect()
ground_truth = ttest_ind(X[labels == 1], X[labels == 2])
assert_array_almost_equal(result[0], ground_truth[0])
def test_mass_univariate_classification_gnb_1d(self):
"""Simple classification problem, 1d features"""
X1 = array([-1, -1, -1.2, 1, 1, 1.2])
X2 = array([-1, -1, 1.2, 1, 1, 1.2])
labels = array([1, 1, 1, 2, 2, 2])
params = dict([('labels', labels)])
clf = MassUnivariateClassifier.load(params, "gaussnaivebayes", cv=0)
# should predict perfectly
data = self.sc.parallelize(zip([1], [X1]))
result = clf.classify(data).map(lambda (_, v): v).collect()
assert_array_almost_equal(result[0], [1.0])
# should predict all but one correctly
data = self.sc.parallelize(zip([1], [X2]))
result = clf.classify(data).map(lambda (_, v): v).collect()
assert_array_almost_equal(result[0], [5.0/6.0])
def test_mass_univariate_classification_gnb_1d(self):
"""Simple classification problem, 1d features"""
X1 = array([-1, -1, -1.2, 1, 1, 1.2])
X2 = array([-1, -1, 1.2, 1, 1, 1.2])
labels = array([1, 1, 1, 2, 2, 2])
params = dict([('labels', labels)])
clf = MassUnivariateClassifier.load(params, "gaussnaivebayes", cv=0)
# should predict perfectly
data = self.sc.parallelize(zip([1], [X1]))
result = clf.classify(data).map(lambda (_, v): v).collect()
assert_array_almost_equal(result[0], [1.0])
# should predict all but one correctly
data = self.sc.parallelize(zip([1], [X2]))
result = clf.classify(data).map(lambda (_, v): v).collect()
assert_array_almost_equal(result[0], [5.0 / 6.0])
def test_mass_univariate_classification_ttest_2d(self):
"""Simple classification problem, 2d features"""
X = array([-1, -2, -0.1, -2, -0.1, -2.1, 1, 1.1, 1, 1, 1.1, 2])
features = array([1, 2, 1, 2, 1, 2, 1, 2, 1, 2, 1, 2])
samples = array([1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6])
labels = array([1, 1, 1, 2, 2, 2])
params = dict([('labels', labels), ('features', features), ('samples', samples)])
clf = MassUnivariateClassifier.load(params, "ttest")
# should match direct calculation using scipy
# test first feature only
data = self.sc.parallelize(zip([1], [X]))
result = clf.classify(data, [[1]]).map(lambda (_, v): v).collect()
ground_truth = ttest_ind(X[features == 1][:3], X[features == 1][3:])
assert_array_almost_equal(result[0], ground_truth[0])
# test both features
result = clf.classify(data, [[1, 2]]).map(lambda (_, v): v).collect()
ground_truth = ttest_ind(vstack((X[features == 1][:3], X[features == 2][:3])).T,
vstack((X[features == 1][3:], X[features == 2][3:])).T)
assert_array_almost_equal(result[0][0], ground_truth[0])
import os
import argparse
import glob
from numpy import array
from thunder.classification import MassUnivariateClassifier
from thunder.utils import load
from thunder.utils import save
from pyspark import SparkContext
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="fit a regression model")
parser.add_argument("datafile", type=str)
parser.add_argument("paramfile", type=str)
parser.add_argument("outputdir", type=str)
parser.add_argument("classifymode", choices="naivebayes", help="form of classifier")
parser.add_argument("--featureset", type=array, default="None", required=False)
parser.add_argument("--cv", type=int, default="0", required=False)
parser.add_argument("--preprocess", choices=("raw", "dff", "dff-highpass", "sub"), default="raw", required=False)
args = parser.parse_args()
sc = SparkContext("classify")
data = load(sc, args.datafile, args.preprocess)
clf = MassUnivariateClassifier.load(args.paramfile, args.classifymode, cv=args.cv)
perf = clf.classify(data, args.featureset)
outputdir = args.outputdir + "-classify"
save(perf, outputdir, "perf", "matlab")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="fit a regression model")
parser.add_argument("datafile", type=str)
parser.add_argument("paramfile", type=str)
parser.add_argument("outputdir", type=str)
parser.add_argument("classifymode",
choices="naivebayes",
help="form of classifier")
parser.add_argument("--featureset",
type=array,
default="None",
required=False)
parser.add_argument("--cv", type=int, default="0", required=False)
parser.add_argument("--preprocess",
choices=("raw", "dff", "dff-highpass", "sub"),
default="raw",
required=False)
args = parser.parse_args()
sc = SparkContext("classify")
data = load(sc, args.datafile, args.preprocess)
clf = MassUnivariateClassifier.load(args.paramfile,
args.classifymode,
cv=args.cv)
perf = clf.classify(data, args.featureset)
outputdir = args.outputdir + "-classify"
save(perf, outputdir, "perf", "matlab")