def runtest(self):
model = RegressionModel.load(os.path.join(self.modelfile, "linear"),
"linear")
betas, stats, resid = model.fit(self.rdd)
result = stats.map(lambda (_, v): float16(v)).collect()
savemat(self.savefile + "tmp.mat",
mdict={"tmp": result},
oned_as='column')
def test_linear_regress(self):
data = self.sc.parallelize([(1, array([1.5, 2.3, 6.2, 5.1, 3.4, 2.1]))])
x = array([
array([1, 0, 0, 0, 0, 0]),
array([0, 1, 0, 0, 0, 0])
])
model = RegressionModel.load(x, "linear")
betas, stats, resid = model.fit(data)
assert(allclose(betas.map(lambda (_, v): v).collect()[0], array([-2.7, -1.9])))
assert(allclose(stats.map(lambda (_, v): v).collect()[0], array([0.42785299])))
assert(allclose(resid.map(lambda (_, v): v).collect()[0], array([0, 0, 2, 0.9, -0.8, -2.1])))
def test_linear_regress(self):
data = self.sc.parallelize([(1, array([1.5, 2.3, 6.2, 5.1, 3.4,
2.1]))])
x = array([array([1, 0, 0, 0, 0, 0]), array([0, 1, 0, 0, 0, 0])])
model = RegressionModel.load(x, "linear")
betas, stats, resid = model.fit(data)
assert (allclose(
betas.map(lambda (_, v): v).collect()[0], array([-2.7, -1.9])))
assert (allclose(
stats.map(lambda (_, v): v).collect()[0], array([0.42785299])))
assert (allclose(
resid.map(lambda (_, v): v).collect()[0],
array([0, 0, 2, 0.9, -0.8, -2.1])))
def test_blinear_regress(self):
data = self.sc.parallelize([(1, array([1.5, 2.3, 6.2, 5.1, 3.4, 2.1]))])
x1 = array([
array([1, 0, 1, 0, 1, 0]),
array([0, 1, 0, 1, 0, 1])
])
x2 = array([
array([1, 1, 0, 0, 0, 0]),
array([0, 0, 1, 1, 0, 0]),
array([0, 0, 0, 0, 1, 1])
])
model = RegressionModel.load((x1, x2), "bilinear")
betas, stats, resid = model.fit(data)
tol = 1E-4 # to handle rounding errors
assert(allclose(betas.map(lambda (_, v): v).collect()[0], array([-3.1249, 5.6875, 0.4375]), atol=tol))
def test_blinear_regress(self):
data = self.sc.parallelize([(1, array([1.5, 2.3, 6.2, 5.1, 3.4,
2.1]))])
x1 = array([array([1, 0, 1, 0, 1, 0]), array([0, 1, 0, 1, 0, 1])])
x2 = array([
array([1, 1, 0, 0, 0, 0]),
array([0, 0, 1, 1, 0, 0]),
array([0, 0, 0, 0, 1, 1])
])
model = RegressionModel.load((x1, x2), "bilinear")
betas, stats, resid = model.fit(data)
tol = 1E-4 # to handle rounding errors
assert (allclose(betas.map(lambda (_, v): v).collect()[0],
array([-3.1249, 5.6875, 0.4375]),
atol=tol))
from thunder.regression import RegressionModel
from thunder.factorization import PCA
from thunder.utils import load
from thunder.utils import save
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="fit a regression model")
parser.add_argument("datafile", type=str)
parser.add_argument("modelfile", type=str)
parser.add_argument("outputdir", type=str)
parser.add_argument("regressmode", choices=("mean", "linear", "bilinear"), help="form of regression")
parser.add_argument("--k", type=int, default=2)
parser.add_argument("--preprocess", choices=("raw", "dff", "sub", "dff-highpass", "dff-percentile"
"dff-detrendnonlin", "dff-detrend-percentile"), default="raw", required=False)
args = parser.parse_args()
sc = SparkContext(appName="regresswithpca")
data = load(sc, args.datafile, args.preprocess)
model = RegressionModel.load(args.modelfile, args.regressmode) # do regression
betas, stats, resid = model.fit(data)
pca = PCA(args.k).fit(betas) # do PCA
traj = model.fit(data, pca.comps) # get trajectories
outputdir = args.outputdir + "-regress"
save(pca.comps, outputdir, "comps", "matlab")
save(pca.latent, outputdir, "latent", "matlab")
save(pca.scores, outputdir, "scores", "matlab")
save(traj, outputdir, "traj", "matlab")
choices=("raw", "dff", "dff-highpass", "sub"),
default="raw",
required=False)
parser.add_argument("--regressmodelfile", type=str)
parser.add_argument("--regressmode",
choices=("linear", "bilinear"),
help="form of regression")
args = parser.parse_args()
sc = SparkContext(args.master, "tuning")
if args.master != "local":
egg = glob.glob(os.path.join(os.environ['THUNDER_EGG'], "*.egg"))
sc.addPyFile(egg[0])
data = load(sc, args.datafile, args.preprocess)
tuningmodel = TuningModel.load(args.tuningmodelfile, args.tuningmode)
if args.regressmodelfile is not None:
# use regression results
regressmodel = RegressionModel.load(args.regressmodelfile,
args.regressmode)
betas, stats, resid = regressmodel.fit(data)
params = tuningmodel.fit(betas)
else:
# use data
params = tuningmodel.fit(data)
outputdir = args.outputdir + "-tuning"
save(params, outputdir, "params", "matlab")
import glob
from thunder.regression import RegressionModel
from thunder.io import load
from thunder.io import save
from pyspark import SparkContext
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="fit a regression model")
parser.add_argument("master", type=str)
parser.add_argument("datafile", type=str)
parser.add_argument("modelfile", type=str)
parser.add_argument("outputdir", type=str)
parser.add_argument("regressmode", choices=("mean", "linear", "bilinear"), help="form of regression")
parser.add_argument("--preprocess", choices=("raw", "dff", "dff-highpass", "sub"), default="raw", required=False)
args = parser.parse_args()
sc = SparkContext(args.master, "regress")
if args.master != "local":
egg = glob.glob(os.path.join(os.environ['THUNDER_EGG'], "*.egg"))
sc.addPyFile(egg[0])
data = load(sc, args.datafile, args.preprocess)
stats, betas, resid = RegressionModel.load(args.modelfile, args.regressmode).fit(data)
outputdir = args.outputdir + "-regress"
save(stats, outputdir, "stats", "matlab")
save(betas, outputdir, "betas", "matlab")
# compute mean map
vals = Stats("mean").calc(data)
save(vals,outputdir,"mean_vals","matlab")
# compute local cor
if args.neighbourhood != 0:
cor = LocalCorr(neighborhood=args.neighbourhood).calc(data)
save(cor,outputdir,"local_corr","matlab")
# if stim argument is not default
if args.stim != '-':
# parse into different stim names
p = re.compile('-')
stims = p.split(args.stim)
# compute regression
for i in range(len(stims)):
modelfile = os.path.join(args.datafolder, args.basename + stims[i])
m = RegressionModel.load(modelfile, args.regressmode)
betas, stats, resid = m.fit(data)
t = TuningModel.load(modelfile, args.tuningmode)
tune = t.fit(betas)
out_name = "stats_" + stims[i]
save(stats, outputdir, out_name, "matlab")
out_name = "tune_" + stims[i]
save(tune, outputdir, out_name, "matlab")
import glob
from thunder.regression import RegressionModel
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("master", type=str)
parser.add_argument("datafile", type=str)
parser.add_argument("modelfile", type=str)
parser.add_argument("outputdir", type=str)
parser.add_argument("regressmode", choices=("mean", "linear", "bilinear"), help="form of regression")
parser.add_argument("--preprocess", choices=("raw", "dff", "dff-highpass", "sub"), default="raw", required=False)
args = parser.parse_args()
sc = SparkContext(args.master, "regress")
if args.master != "local":
egg = glob.glob(os.path.join(os.environ['THUNDER_EGG'], "*.egg"))
sc.addPyFile(egg[0])
data = load(sc, args.datafile, args.preprocess)
stats, betas, resid = RegressionModel.load(args.modelfile, args.regressmode).fit(data)
outputdir = args.outputdir + "-regress"
save(stats, outputdir, "stats", "matlab")
save(betas, outputdir, "betas", "matlab")
def runtest(self):
model = RegressionModel.load(os.path.join(self.modelfile, "linear"),
"linear")
betas, stats, resid = model.fit(self.rdd)
stats.count()
def runtest(self):
model = RegressionModel.load(os.path.join(self.modelfile, "linear"), "linear")
betas, stats, resid = model.fit(self.rdd)
result = stats.map(lambda (_, v): float16(v)).collect()
savemat(self.savefile + "tmp.mat", mdict={"tmp": result}, oned_as='column')
def runtest(self):
model = RegressionModel.load(os.path.join(self.modelfile, "linear"), "linear")
betas, stats, resid = model.fit(self.rdd)
stats.count()
