def generate_scipy_comparison(csvPathname, col=0, h2oMedian=None, h2oMedian2=None):
# this is some hack code for reading the csv and doing some percentile stuff in scipy
# from numpy import loadtxt, genfromtxt, savetxt
import numpy as np
import scipy as sp
dataset = np.genfromtxt(
open(csvPathname, 'r'),
delimiter=',',
# skip_header=1,
dtype=None); # guess!
print "csv read for training, done"
# we're going to strip just the last column for percentile work
# used below
NUMCLASSES = 10
print "csv read for training, done"
# data is last column
# drop the output
print dataset.shape
if len(dataset.shape) > 1:
target = [x[col] for x in dataset]
else:
target = dataset
# we may have read it in as a string. coerce to number
targetFP = np.array(target, np.float)
if 1==0:
n_features = len(dataset[0]) - 1;
print "n_features:", n_features
# get the end
# target = [x[-1] for x in dataset]
# get the 2nd col
print "histogram of target"
print target
print sp.histogram(target, bins=NUMCLASSES)
print target[0]
print target[1]
thresholds = [0.001, 0.01, 0.1, 0.25, 0.33, 0.5, 0.66, 0.75, 0.9, 0.99, 0.999]
print "scipy per:", thresholds
from scipy import stats
# a = stats.scoreatpercentile(target, per=per)
a = stats.mstats.mquantiles(targetFP, prob=thresholds)
a2 = ["%.2f" % v for v in a]
h2p.red_print("scipy stats.mstats.mquantiles:", a2)
# also get the median with a painful sort (h2o_summ.percentileOnSortedlist()
# inplace sort
targetFP.sort()
b = h2o_summ.percentileOnSortedList(targetFP, 0.50 if DO_MEDIAN else 0.999, interpolate='linear')
label = '50%' if DO_MEDIAN else '99.9%'
h2p.blue_print(label, "from sort:", b)
s = a[5 if DO_MEDIAN else 10]
h2p.blue_print(label, "from scipy:", s)
h2p.blue_print(label, "from h2o summary2:", h2oMedian)
h2p.blue_print(label, "from h2o quantile multipass:"******"%.2f" % v for v in a]
h2p.red_print("after sort")
h2p.red_print("scipy stats.mstats.mquantiles:", a2)
# this is type 7
alphap = 1
betap = 1
from scipy import stats
a1 = stats.scoreatpercentile(target,
per=100 * OTHER_T,
interpolation_method='fraction')
h2p.red_print("stats.scoreatpercentile:", a1)
a2 = stats.mstats.mquantiles(targetFP,
prob=[OTHER_T],
alphap=alphap,
betap=betap)
h2p.red_print("scipy stats.mstats.mquantiles:", a2)
targetFP.sort()
b = h2o_summ.percentileOnSortedList(targetFP, OTHER_T, interpolate='linear')
h2p.red_print("sort algo:", b)
h2p.red_print("from h2o (multi):", quantiles[0])
print "Now looking at the sorted list..same thing"
h2p.blue_print("stats.scoreatpercentile:", a1)
a2 = stats.mstats.mquantiles(targetFP,
prob=[OTHER_T],
alphap=alphap,
betap=betap)
h2p.blue_print("scipy stats.mstats.mquantiles:", a2)
b = h2o_summ.percentileOnSortedList(targetFP, OTHER_T, interpolate='linear')
h2p.blue_print("sort algo:", b)
h2p.blue_print("from h2o (multi):", quantiles[0])
QUANTILE = 0.25
print "stress the 1000 fixed binning based on (max-min)/1000"
a = [
-1.0000002e10, -1.0000001e10, -1.0000000e10, -1.0000002e9, -1.0000001e9,
-1.0000000e9, -1.0000002e6, -1.0000001e6, -1.0000000e6, -1.0000002e3,
-1.0000001e3, -1.0000000e3, -1.0, 0.0000000, 1.0, 1.0000002e3, 1.0000001e3,
1.0000000e3, 1.0000002e6, 1.0000001e6, 1.0000000e6, 1.0000002e9,
1.0000001e9, 1.0000000e9, 1.0000002e10, 1.0000001e10, 1.0000000e10
]
initList = ["ddd = c(%s)" % ",".join(map(str, a))]
# get expected result
a.sort()
expectedP = h2o_summ.percentileOnSortedList(a, QUANTILE, interpolate='linear')
print "expectedP:", expectedP
h2p.blue_print("sort result, expectedP:", expectedP)
exprList = [
("abc = quantile(ddd[,1], c(%s))" % QUANTILE, 1),
]
class Basic(unittest.TestCase):
def tearDown(self):
h2o.check_sandbox_for_errors()
@classmethod
def setUpClass(cls):
global SEED
1.0000000e6,
1.0000002e9,
1.0000001e9,
1.0000000e9,
1.0000002e10,
1.0000001e10,
1.0000000e10
]
initList = [
"ddd = c(%s)" % ",".join(map(str,a))
]
# get expected result
a.sort()
expectedP = h2o_summ.percentileOnSortedList(a, QUANTILE, interpolate='linear')
print "expectedP:", expectedP
h2p.blue_print("sort result, expectedP:", expectedP)
exprList = [
("abc = quantile(ddd[,1], c(%s))" % QUANTILE, 1),
]
class Basic(unittest.TestCase):
def tearDown(self):
h2o.check_sandbox_for_errors()
@classmethod
def setUpClass(cls):
global SEED
SEED = h2o.setup_random_seed()
# an approx? (was good when comparing to h2o type 2)
alphap=0.4
betap=0.4
# this is type 7
alphap=1
betap=1
from scipy import stats
a1 = stats.scoreatpercentile(target, per=100*OTHER_T, interpolation_method='fraction')
h2p.red_print("stats.scoreatpercentile:", a1)
a2 = stats.mstats.mquantiles(targetFP, prob=[OTHER_T], alphap=alphap, betap=betap)
h2p.red_print("scipy stats.mstats.mquantiles:", a2)
targetFP.sort()
b = h2o_summ.percentileOnSortedList(targetFP, OTHER_T, interpolate='linear')
h2p.red_print("sort algo:", b)
h2p.red_print( "from h2o (multi):", quantiles[0])
print "Now looking at the sorted list..same thing"
h2p.blue_print("stats.scoreatpercentile:", a1)
a2 = stats.mstats.mquantiles(targetFP, prob=[OTHER_T], alphap=alphap, betap=betap)
h2p.blue_print("scipy stats.mstats.mquantiles:", a2)
b = h2o_summ.percentileOnSortedList(targetFP, OTHER_T, interpolate='linear')
h2p.blue_print("sort algo:", b)
h2p.blue_print( "from h2o (multi):", quantiles[0])
