def slicer(self):
if self.cache is None:
self.cache = np.empty(len(self), dtype=self.factor.slicer.dtype)
slices = self.factor.slicer(0, len(self.factor), SLICELEN)
def f(offset, slice):
n = len(slice)
self.cache[offset:(offset + n)] = slice
return offset + n
Seq.reduce(f, 0, slices)
return VectorSlicer(self.cache)
def xgblogit(label,
factors,
trainselector=None,
mu=0.5,
eta=0.1,
lambda_=1.0,
gamma=0.0,
maxdepth=2,
nrounds=2,
minh=1.0,
slicelen=10000):
f0 = np.empty(len(label), dtype=np.float32)
f0.fill(logitraw(mu))
label = label.to_array()
g = np.zeros(len(f0), dtype=np.float32)
h = np.zeros(len(f0), dtype=np.float32)
def step(x, m):
fm, trees = x
if trainselector is not None:
get_gh_sel(trainselector, fm, label, g, h)
else:
get_gh(fm, label, g, h)
g_cov = Covariate.from_array(g)
h_cov = Covariate.from_array(h)
tree, predraw = growtree(factors, g_cov, h_cov, fm, eta, maxdepth,
lambda_, gamma, minh, slicelen)
trees.append(tree)
return (predraw, trees)
fm, trees = Seq.reduce(step, (f0, []),
Seq.from_gen((i for i in range(nrounds))))
get_pred(fm)
return trees, fm
def get_hist_slice(gsum0, hsum0, nodeids, nodecansplit, factor, gcovariate, hcovariate,
start, length, slicelen):
nodeslices = VectorSlicer(nodeids)(start, length, slicelen)
factorslices = factor.slicer(start, length, slicelen)
gslices = gcovariate.slicer(start, length, slicelen)
hslices = hcovariate.slicer(start, length, slicelen)
zipslices = Seq.zip(nodeslices, factorslices, gslices, hslices)
return Seq.reduce(f_hist, (gsum0, hsum0, nodecansplit), zipslices)
def __repr__(self):
slices = self.slicer(0, min(HEADLENGTH, len(self)), HEADLENGTH)
def f(acc, slice):
return acc + ' '.join([str(v) for v in slice]) + " "
datahead = Seq.reduce(f, "", slices)
return "BoolVariate {var} with {len} obs: {head}".format(var=self.name,
len=len(self),
head=datahead)
def get_freq(self):
slices = self.slicer(0, len(self), SLICELEN)
counts0 = np.zeros(len(self.levels), dtype=np.int32)
@jit(nopython=True, cache=True)
def f(acc, slice):
for v in slice:
acc[v] = acc[v] + 1
return acc
return Seq.reduce(f, counts0, slices)
def __repr__(self):
k = min(HEADLENGTH, len(self))
slices = self.slicer(0, k, HEADLENGTH)
levels = self.levels
def f(acc, slice):
return acc + ' '.join([levels[i] for i in slice]) + " "
datahead = Seq.reduce(f, "", slices)
s = "" if k == len(self) else "..."
return "Factor {f} with {len} obs and {n} levels: {head}{s}".format(
f=self.name, len=len(self), head=datahead, n=len(levels) - 1, s=s)
def __repr__(self):
k = min(HEADLENGTH, len(self))
slices = self.slicer(0, k, HEADLENGTH)
def f(acc, slice):
return acc + ' '.join(
["." if np.isnan(v) else str(v) for v in slice]) + " "
datahead = Seq.reduce(f, "", slices)
s = "" if k == len(self) else "..."
return "Covariate {cov} with {len} obs: {head}{s}".format(
cov=self.name, len=len(self), head=datahead, s=s)
def unique(self):
slices = self.slicer(0, len(self), SLICELEN)
# v, tail = Seq.try_read(slices)
# set0 = set(v)
# @jit(nopython=True, cache=True)
# def f(acc, slice):
# for v in slice:
# if not np.isnan(v):
# acc.add(v)
# return acc
# res = Seq.reduce(f, set0, tail)
dt = types.float32 if self.slicer.dtype == np.float32 else types.float64
set0 = Dict.empty(key_type=dt, value_type=dt)
res = Seq.reduce(f_unique, set0, slices)
arr = np.array(list(res.keys()), dtype=self.slicer.dtype)
arr.sort()
return arr
