def get_catalog():
pth = os.path.join(os.path.dirname(__file__), 'data', 'catalog.pkl')
if running_on_cloud():
return pickle.load(bucket.getf('catalog.pkl'))
return pickle.load(open(pth))
def get_catalog():
pth = os.path.join(os.path.dirname(__file__), 'data', 'catalog.pkl')
if running_on_cloud():
return pickle.load(bucket.getf('catalog.pkl'))
return pickle.load(open(pth))
def negatives_iterator(self, samples_per_field=None):
"""Yield an infinite sequence of offset stamp parameters
Parameters
----------
samples_per_field : int (optional)
How many samples to generate before switching
longitues by >1 deg. Tuning this can improve
IO performance.
Yields
------
An infinite sequence of (field_lon, lcen, bcen, rad)
"""
cat = get_catalog()
l0, b0, r0 = cat[:, 0], cat[:, 1], cat[:, 3]
while True:
lon = self._random_field()
nsample = samples_per_field
if nsample is None:
nsample = 30000 if running_on_cloud() else 500
l = np.random.uniform(-.5, .5, nsample) + lon
b = np.random.uniform(-.8, .8, nsample)
r = np.random.choice(r0, size=nsample)
bad = overlap(l, b, r, l0, b0, r0)
l, b, r = l[~bad], b[~bad], r[~bad]
for i in range(l.size):
yield lon, l[i], b[i], r[i]
def negatives_iterator(self, samples_per_field=None):
"""Yield an infinite sequence of offset stamp parameters
Parameters
----------
samples_per_field : int (optional)
How many samples to generate before switching
longitues by >1 deg. Tuning this can improve
IO performance.
Yields
------
An infinite sequence of (field_lon, lcen, bcen, rad)
"""
cat = get_catalog()
l0, b0, r0 = cat[:, 0], cat[:, 1], cat[:, 3]
while True:
lon = self._random_field()
nsample = samples_per_field
if nsample is None:
nsample = 30000 if running_on_cloud() else 500
l = np.random.uniform(-.5, .5, nsample) + lon
b = np.random.uniform(-.8, .8, nsample)
r = np.random.choice(r0, size=nsample)
bad = overlap(l, b, r, l0, b0, r0)
l, b, r = l[~bad], b[~bad], r[~bad]
for i in range(l.size):
yield lon, l[i], b[i], r[i]
def _has_field(lon):
"""Return True if image data exists for a given longitude"""
base = os.path.join(os.path.dirname(__file__), 'data', 'galaxy',
'registered')
if not running_on_cloud():
return os.path.exists(os.path.join(base, "%3.3i_i4.fits" % lon)) and \
os.path.exists(os.path.join(base, "%3.3i_mips.fits" % lon))
else:
return bucket.exists("%3.3i_i4.fits" % lon) and \
bucket.exists("%3.3i_mips.fits" % lon)
def _has_field(lon):
"""Return True if image data exists for a given longitude"""
base = os.path.join(os.path.dirname(__file__), 'data', 'galaxy',
'registered')
if not running_on_cloud():
return os.path.exists(os.path.join(base, "%3.3i_i4.fits" % lon)) and \
os.path.exists(os.path.join(base, "%3.3i_mips.fits" % lon))
else:
return bucket.exists("%3.3i_i4.fits" % lon) and \
bucket.exists("%3.3i_mips.fits" % lon)
def get_field(lon):
"""Create and return a new field appropriate
for running locally or on PiCloud
The previous return value is cached,
to avoid repeated I/O for repeated
requests for the same field
"""
global _cached_field
if _cached_field is not None and _cached_field.lon == lon:
return _cached_field
del _cached_field
logging.getLogger(__name__).debug("Loading a new field at l=%i" % lon)
if running_on_cloud():
result = CloudField(lon)
else:
result = Field(lon)
_cached_field = result
return result
def get_field(lon):
"""Create and return a new field appropriate
for running locally or on PiCloud
The previous return value is cached,
to avoid repeated I/O for repeated
requests for the same field
"""
global _cached_field
if _cached_field is not None and _cached_field.lon == lon:
return _cached_field
del _cached_field
logging.getLogger(__name__).debug("Loading a new field at l=%i" % lon)
if running_on_cloud():
result = CloudField(lon)
else:
result = Field(lon)
_cached_field = result
return result
def random_iterator(self, samples_per_field=None):
"""Yield an infinite sequence of random stamp parameters
The random sample is generated using Field.random_stamps
Parameters
----------
samples_per_field : int (optional)
How many samples to generate before switching
longitues by >1 deg. Tuning this can improve
IO performance.
Yields
------
An infinite sequence of (field_lon, lcen, bcen, rad)
"""
while True:
lon = self._random_field()
nsample = samples_per_field
if nsample is None:
nsample = 30000 if running_on_cloud() else 500
for f in get_field(lon).random_stamps(nsample):
yield f
def random_iterator(self, samples_per_field=None):
"""Yield an infinite sequence of random stamp parameters
The random sample is generated using Field.random_stamps
Parameters
----------
samples_per_field : int (optional)
How many samples to generate before switching
longitues by >1 deg. Tuning this can improve
IO performance.
Yields
------
An infinite sequence of (field_lon, lcen, bcen, rad)
"""
while True:
lon = self._random_field()
nsample = samples_per_field
if nsample is None:
nsample = 30000 if running_on_cloud() else 500
for f in get_field(lon).random_stamps(nsample):
yield f
import csv
import json
import os
import pickle
from os.path import join as path_join
from os.path import isfile
import joblib
import numpy as np
from sklearn.preprocessing import LabelEncoder
from scipy.io import mmread
from sklearn.metrics import mean_absolute_error
from sklearn.base import clone
from scipy.sparse import coo_matrix, hstack, vstack
try:
import cloud
on_cloud = cloud.running_on_cloud()
except Exception as e:
on_cloud = False
#def read_column(filename, column_name):
#"""returns generator with values in column_name in filename"""
#csv_file = csv.reader(open(filename, 'r'))
#header = csv_file.next()
#print header
#if column_name not in header:
#raise Exception("Column '%s' is not in header! Header: %s" % (column_name, ",".join(header)))
#column_index = header.index(column_name)
#for line in csv_file:
#yield line[column_index]
def data_path():
if cloud.running_on_cloud():
return "/bucket/"
else:
return os.path.join(os.path.dirname(__file__), "../../data/")
import os
#import PyWiseRF
from sklearn.ensemble import RandomForestClassifier
import cloud
import numpy as np
if cloud.running_on_cloud():
os.environ['WISERF_ROOT'] = '/home/picloud/WiseRF-1.5.9-linux-x86_64-rc2'
def test():
x = np.random.random((5, 5))
y = np.array([1, 1, 1, 0, 0])
clf = WiseRF().fit(x, y)
return clf
#class WiseRF(PyWiseRF.WiseRF):
# def decision_function(self, x):
# p = self.predict_proba(x)
# return p[:, 1] - p[:, 0]
class WiseRF(RandomForestClassifier):
def decision_function(self, x):
p = self.predict_proba(x)
return p[:, 1] - p[:, 0]
import os
import PyWiseRF
import cloud
import numpy as np
if cloud.running_on_cloud():
os.environ['WISERF_ROOT'] = '/home/picloud/WiseRF-1.5.9-linux-x86_64-rc2'
def test():
x = np.random.random((5, 5))
y = np.array([1, 1, 1, 0, 0])
clf = WiseRF().fit(x, y)
return clf
class WiseRF(PyWiseRF.WiseRF):
def decision_function(self, x):
p = self.predict_proba(x)
return p[:, 1] - p[:, 0]
