def initScore_unbiased(sampleindex, D1index, k, sr, queries):
"""
Unbiased benefit estimation.
:param sampleindex: inverted index of sample
:param k: top-k restriction
:param sr: sample rate
:param Dratio: local database rate
:param queries: query pool
:return: query pool with biased benefit
"""
query_pool = maxpq()
for q, l1 in queries.iteritems():
if len(sampleindex[q]) != 0:
ls = len(sampleindex[q])
lcap = len(sampleindex[q].intersection(D1index[q]))
est_score = lcap / sr
if est_score > k:
score = lcap * k / (ls * 1.0)
else:
score = est_score
else:
score = 0
query_pool[q] = score
return query_pool
def initScore_biased(sampleindex, k, sr, Dratio, queries):
"""
Biased benefit estimation.
:param sampleindex: inverted index of sample
:param k: top-k restriction
:param sr: sample rate
:param Dratio: local database rate
:param queries: query pool
:return: query pool with biased benefit
"""
query_pool = maxpq()
for q, l1 in queries.iteritems():
if len(sampleindex[q]) != 0:
ls = len(sampleindex[q])
est_score = ls / sr
if est_score > k:
score = k * l1 / (est_score * 1.0)
else:
score = l1
else:
if l1 > k * Dratio:
score = 1.0 * k * Dratio
else:
score = l1
query_pool[q] = score
return query_pool
def _immunize_deg(adj, num_nodes, queue=True):
"""Internal function. To immunize a graph, use `immunize`."""
# Takes O(n) time and O(n) space
deg = {n: len(adj[n]) for n in adj}
# Make sure we don't remove more nodes than there are available
num_nodes = min(num_nodes, len(deg))
# If using a queue, we need to heapify it (which takes O(n))
if queue:
deg = maxpq(deg)
# Main loop
removed = []
for _ in range(num_nodes):
if queue:
node = deg.pop() # Takes O(log n)
else:
node = max(deg, key=deg.get) # Takes O(n)
del deg[node]
# Takes O(degree[node])
for neigh in adj[node]:
adj[neigh].remove(node)
deg[neigh] -= 1
# Finally, finish udpating the graph, and store the node.
del adj[node]
removed.append(node)
return removed
def __init__(self, protocol, fact, parents=None, barzer_svc=None):
super(ConvoCompositeFact, self).__init__(protocol, fact, parents, barzer_svc=barzer_svc)
self.text = fact.text
op = self.OPERATOR_MAP.get(fact.operator)
if op:
self.op = op()
self.pq = pqdict.maxpq()
self.set_children([
protocol.create_or_update_fact(f, [self]) for f in fact.facts])
self.value = self.op.calc(children=self.get_children())
self.confidence = self.op.confidence(children=self.get_children())
def __init__(self):
self.query = input("Enter search query: ")
self.webpages_limit = input(
"Set total number of webpages to be crawled: ")
self.limit = input(
"Set limits on how many webpages be crawled from single site: ")
self.priority_queue = maxpq()
self.queue = queue.Queue()
self.downloader = Downloader()
self.parser = Parser(self.query)
self.calculator = Calculator(self.query)
self.relevance = Relevance()
self.webpages_crawled = 0
self.logger = logging.getLogger(__name__)
self.visited_urls = set()
self.sites_times = {}
def test_equality():
# eq
pq1 = pqdict(sample_items)
pq2 = pqdict(sample_items)
assert pq1 == pq2
assert not pq1 != pq2
# ne
pq2[random.choice(sample_keys)] += 1
assert not pq1 == pq2
assert pq1 != pq2
# pqdict == regular dict if they have same key/value pairs
adict = dict(sample_items)
assert pq1 == adict
# TODO: FIX?
# pqdicts evaluate as equal even if they have different
# key functions and/or precedence functions
pq3 = maxpq(sample_items)
assert pq1 == pq3
def test_equality(self):
# eq
pq1 = pqdict(sample_items)
pq2 = pqdict(sample_items)
self.assertTrue(pq1 == pq2)
self.assertFalse(pq1 != pq2)
# ne
pq2[random.choice(sample_keys)] += 1
self.assertFalse(pq1 == pq2)
self.assertTrue(pq1 != pq2)
# pqdict == regular dict if they have same key/value pairs
adict = dict(sample_items)
self.assertEqual(pq1, adict)
# TODO: FIX?
# pqdicts evaluate as equal even if they have different
# key functions and/or precedence functions
pq3 = maxpq(sample_items)
self.assertEqual(pq1, pq3)
def test_equality(self):
# eq
pq1 = pqdict(sample_items)
pq2 = pqdict(sample_items)
self.assertTrue(pq1 == pq2)
self.assertFalse(pq1 != pq2)
# ne
pq2[random.choice(sample_keys)] += 1
self.assertFalse(pq1 == pq2)
self.assertTrue(pq1 != pq2)
# pqdict == regular dict if they have same key/value pairs
adict = dict(sample_items)
self.assertEqual(pq1, adict)
# TODO: FIX?
# pqdicts evaluate as equal even if they have different
# key functions and/or precedence functions
pq3 = maxpq(sample_items)
self.assertEqual(pq1, pq3)
def __init__(self, data, barzer_svc=None):
protocol = Protocol(data)
self.id = 'protocol'
self.index = cg_index.Index()
super(ConvoProtocol, self).__init__()
self.terminals = {}
self.facts = defaultdict(set)
self.barzer_svc = barzer_svc or default_barzer_instance
self.visited_facts = set()
self.facts_to_update = deque()
for t in protocol.terminals:
self.facts[t.id] = self.terminals[t.id] = ConvoCompositeFact(protocol=self, fact=t, parents=[self])
self.set_children(self.terminals.values())
self.pq = pqdict.maxpq()
for t in self.terminals.values():
self.pq[t] = t.score()
def test_maxpq():
pq = maxpq(A=5, B=8, C=7, D=3, E=9, F=12, G=1)
assert list(pq.popvalues()) == [12, 9, 8, 7, 5, 3, 1]
assert pq.precedes == operator.gt
def test_maxpq(self):
pq = maxpq(A=5, B=8, C=7, D=3, E=9, F=12, G=1)
self.assertEqual(list(pq.popvalues()), [12, 9, 8, 7, 5, 3, 1])
self.assertEqual(pq.precedes, operator.gt)
def _immunize_ci(adj, num_nodes, queue=True):
"""Internal function. To immunize a graph, use `immunize`."""
# Once populated, will take O(n) space
ci = defaultdict(int)
# Takes O(m) time
for node in adj:
excess_deg = len(adj[node]) - 1
for neigh in adj[node]:
ci[node] += excess_deg * (len(adj[neigh]) - 1)
# Make sure we don't remove more nodes than there are available
num_nodes = min(num_nodes, len(ci))
# If using a queue, we need to heapify it (which takes O(n))
if queue:
ci = maxpq(ci)
# Main loop
removed = []
for _ in range(num_nodes):
if queue:
node = ci.pop() # Takes O(log n)
else:
node = max(ci, key=ci.get) # Takes O(n)
del ci[node]
# Takes O(degree[node])
for neigh in adj[node]:
adj[neigh].remove(node)
# Compute the deltas. Takes O(degree^2[node])
deg = len(adj[node])
delta = defaultdict(int)
count = defaultdict(int)
for neigh in adj[node]:
if len(adj[neigh]) > 0:
delta[neigh] += \
((deg - 1) * (len(adj[neigh]) - 1)
+ ci[neigh] // (len(adj[neigh])))
else:
# Nodes of degree 1 decrease all the way to zero
delta[neigh] += ci[neigh]
for neigh2 in adj[neigh]:
if neigh2 in adj[node]:
delta[neigh2] += len(adj[neigh2])
else:
delta[neigh2] += len(adj[neigh2]) - 1
count[neigh2] += 1
# Apply the changes at the same time. Takes O(degree^2[node]).
for neigh2 in delta:
# At the end of the previous loop, nodes of type 1 and 2
# already have the correct deltas, while nodes of type 3 are
# missing a term.
if neigh2 in adj[node] and count[neigh2] > 0:
delta[neigh2] -= count[neigh2]
# If dict, takes O(1). If heap, takes O(log n)
ci[neigh2] = ci[neigh2] - delta[neigh2]
# Finally, finish udpating the graph, and store the node.
del adj[node]
removed.append(node)
return removed
def _immunize_xdeg(adj, num_nodes, queue=True):
"""Internal function. To immunize a graph, use `immunize`."""
# Takes O(m) time and O(2n) space
to_be_squared, sum_squares = defaultdict(int), defaultdict(int)
for node in adj:
for neigh in adj[node]:
# Note we keep the sum of squares, not its square, and square
# it only when needed.
to_be_squared[node] += len(adj[neigh]) - 1
sum_squares[node] += (len(adj[neigh]) - 1)**2
# Takes O(n) time and O(n) space
# Remember to square the first term
xdeg = {n: to_be_squared[n]**2 - sum_squares[n] for n in adj}
# We actually don't need this again
del sum_squares
# Make sure we don't remove more nodes than there are available
num_nodes = min(num_nodes, len(xdeg))
# If using a queue, we need to heapify it (which takes O(n))
if queue:
xdeg = maxpq(xdeg)
# Main loop
# Takes O(m) time and O(m) space
removed = []
for _ in range(num_nodes):
if queue:
node = xdeg.pop() # Takes O(log n)
else:
node = max(xdeg, key=xdeg.get) # Takes O(n)
del xdeg[node]
# Takes O(degree[node])
for neigh in adj[node]:
adj[neigh].remove(node)
# The following loop will compute the difference in xdeg of each
# node, without changing any of the variables. The loop after that
# will actually apply the changes.
#
# We do this as follows. For a node i, define s(i) to be the sum of
# the excess degrees of its neighbors, i.e. s(i) ==
# to_be_squared[i]. Let deg be the degree of the target node. There
# are four types of nodes that will be affected by the removal:
#
# 1. The nodes that are 1hop neighbors but not 2hop heighbors of
# the target node will have their degree decrase by 1, and their
# xdeg decreased by 2(s(i) - deg + 1)(deg - 1).
#
# 2. The nodes that are 2hop neighbors but not 1hop neighbors of
# the target node will have their degree decreased by t(i), where
# t(i) is the number of common neighbors they share with the target
# node (i.e. the number of paths of length 2 between the target
# node and i). Their xdeg will decrease by 2t(i)s(i) + t(i) -
# t(i)**2 - 2p(i). Here, p(i) is the sum of the excess degrees of
# the neighbors of i who are also neighbors of the target node.
#
# 3. The nodes that are both 1hop and 2hop neighbors will have
# their degree decrease by t(i) + deg - 1. Their xdeg will decrease
# by 2t(i)s(i) + t(i) - t(i)**2 - 2p(i) + 2(s(i) - deg + 1)(deg -
# 1) - 2 t(i)(deg - 1). Note this is the sum of the changes for
# 1hop and 2hop neighbors, plus the additional term 2 t(i)(deg - 1).
#
# 4. The target node itself will be removed: its degree and xdeg
# will decrase to zero.
deg = len(adj[node])
delta = defaultdict(int)
count = defaultdict(int)
# Compute the deltas. Takes O(degree^2[node])
for neigh in adj[node]:
delta[neigh] += \
(2 # 2
* (to_be_squared[neigh] - deg + 1) # (s - deg + 1)
* (deg - 1)) # (deg - 1)
for neigh2 in adj[neigh]:
# Each time r we visit a node i through a 2hop path, we are
# adding 2s(i) + 1 - (2r + 1) - 2(p_r - 1), where p_r is
# the degree of the node that led us to i. After visiting
# t(i) times, this adds up to 2t(i)s(i) + t(i) - t(i)**2 -
# 2p(i), as desired. Note that p_r is the degree BEFORE any
# changes have been made to the network, but the degrees of
# the neighbors of the target node already changed in the
# previous loop, therefore p_r - 1 = len(adj[neigh]).
delta[neigh2] += \
(2 * to_be_squared[neigh2] # 2s
+ 1 # + 1
- (2 * count[neigh2] + 1) # - (2r + 1)
- 2 * len(adj[neigh])) # - 2(p_r - 1)
# Increment the count r(i). At the end of this double loop,
# we will have count[i] == t(i).
count[neigh2] += 1
# Apply the changes at the same time. Takes O(degree^2[node]).
for neigh2 in delta:
# At the end of the previous loop, nodes of type 1 and 2
# already have the correct deltas, while nodes of type 3 are
# missing a term. We can finally update xdeg.
if neigh2 in adj[node] and count[neigh2] > 0:
delta[neigh2] -= 2 * count[neigh2] * (deg - 1)
# If dict, takes O(1). If heap, takes O(log n)
xdeg[neigh2] = xdeg[neigh2] - delta[neigh2]
# Update the s(i) of each node. Note this is simply computing
# the changes in i's neighbors' degrees. Note nodes of type 3
# receive both updates.
if neigh2 in adj[node]:
to_be_squared[neigh2] -= deg - 1
if count[neigh2] > 0:
to_be_squared[neigh2] -= count[neigh2]
# Finally, finish udpating the graph, and store the node.
del adj[node]
del to_be_squared[node]
removed.append(node)
return removed
def test_maxpq(self):
pq = maxpq(A=5, B=8, C=7, D=3, E=9, F=12, G=1)
self.assertEqual(
list(pq.popvalues()),
[12, 9, 8 ,7, 5, 3, 1])
self.assertEqual(pq.precedes, operator.gt)
def choose_edges_for_target_coord_num(network, n_coord):
"""
Given a network and an array of required coordination number for each vertex, returns a list of edges that would
(approximately) satisfy the given coordination number.
Parameters
----------
network: PoreNetwork
n_coord: ndarray
Returns
-------
out: ndarray
Index array of the chosen throats
Notes
_____
Implementation of algorithm as described in ROBERT M. SOK ET AL. 2002 Section 4.1.2
"""
WHITE = 0
GRAY = 1
BLACK = 2
tube_marker = np.ones(network.nr_t, dtype=np.int)*GRAY
n_avail = network.nr_nghs
n_white = np.zeros(network.nr_p, dtype=np.int)
assert np.all(n_coord < network.nr_nghs)
def priority_tube(ti):
pi_1, pi_2 = network.edgelist[ti, :]
ns_1 = n_coord[pi_1] - n_white[pi_1]
ns_2 = n_coord[pi_2] - n_white[pi_2]
fs_1 = n_avail[pi_1] - n_white[pi_1]
fs_2 = n_avail[pi_2] - n_white[pi_2]
Fs_1 = 1. - float(ns_1) / float(fs_1)
Fs_2 = 1. - float(ns_2) / float(fs_2)
return 1. / (1. + Fs_1 * Fs_2)
# Initialize priority queue
pq = maxpq()
for ti in xrange(network.nr_t):
pq[ti] = priority_tube(ti)
while pq:
# Pop tube and mark it as white
ti, _ = pq.popitem()
tube_marker[ti] = WHITE
# update n_white
pi_1, pi_2 = network.edgelist[ti, :]
n_white[[pi_1, pi_2]] += 1
# If the target coordination number is reached, mark other tubes as black and delete
for pi in [pi_1, pi_2]:
if n_white[pi] == n_coord[pi]:
for ti in network.ngh_tubes[pi]:
if tube_marker[ti] == GRAY:
tube_marker[ti] = BLACK
del pq[ti]
# Update priorities of adj GRAY tubes
for pi in network.edgelist[ti]:
for ti in network.ngh_tubes[pi]:
if tube_marker[ti] == GRAY:
pq[ti] = priority_tube(ti)
assert np.all(n_white <= n_coord)
print "Unsuccessful tubes", np.sum(n_coord - n_white)
print "Total number of tubes", np.sum(n_white)
return (tube_marker == WHITE).nonzero()[0]
