def count_clicks(src): print "Processing %s" % src total = 0 data = read_vm_file(src) for referrer, target, num_clicks in data: total += num_clicks return total
def read_counts(src): print "Processing %s" % src data = read_vm_file(src) counts = [] for _, _, num_clicks in data: counts.append(num_clicks) return counts
def change_domain_levels(src, dest, new_domain_level):
print "Processing", src
data = read_vm_file(src)
clicks = {}
for referrer, target, num_clicks in data:
if (
referrer is None
or target is None
or num_clicks == -1
or domain_level(referrer) == 1
or domain_level(target) == 1
):
continue
newr = change_domain_level(referrer, new_domain_level)
newt = change_domain_level(target, new_domain_level)
if newr not in clicks:
clicks[newr] = {}
if newt not in clicks[newr]:
clicks[newr][newt] = 0
clicks[newr][newt] += num_clicks
with open(dest, "w") as destf:
writer = csv.writer(destf, delimiter="\t")
for referrer in clicks:
for target in clicks[referrer]:
writer.writerow([referrer, target, clicks[referrer][target]])
def convert_to_json(src, dest):
print "Processing %s" % src
data = read_vm_file(src)
click_counts = {}
for referrer, target, num_clicks in data:
if target not in click_counts:
click_counts[target] = 0
click_counts[target] += num_clicks
total_count = float(numpy.sum(click_counts.values()))
sorted_counts = sorted(click_counts.items(), key=lambda tupl: tupl[1], reverse=True)
cum_count = 0
json = ""
for target, count in sorted_counts:
cum_count += count
json += ' {"name": "%s", "size": %d}' % (target, count)
if cum_count / total_count > .3:
json += "\n"
break
else:
json += ",\n"
with open(dest, 'w') as destf:
destf.write("""{
"name": "entropy",
"children": [
%s
]
}
""" % json)
def convert_to_json(src, dest):
print "Processing %s" % src
data = read_vm_file(src)
click_counts = {}
for referrer, target, num_clicks in data:
if target not in click_counts:
click_counts[target] = 0
click_counts[target] += num_clicks
total_count = float(numpy.sum(click_counts.values()))
sorted_counts = sorted(click_counts.items(),
key=lambda tupl: tupl[1],
reverse=True)
cum_count = 0
json = ""
for target, count in sorted_counts:
cum_count += count
json += ' {"name": "%s", "size": %d}' % (target, count)
if cum_count / total_count > .3:
json += "\n"
break
else:
json += ",\n"
with open(dest, 'w') as destf:
destf.write("""{
"name": "entropy",
"children": [
%s
]
}
""" % json)
def compute_traffic_volume(src):
print "Processing %s" % src
total = 0
data = read_vm_file(src)
for referrer, target, num_clicks in data:
total += num_clicks
return total
def compute_traffic_volume(src): print "Processing %s" % src total = 0 data = read_vm_file(src) for referrer, target, num_clicks in data: total += num_clicks return total
def count_clicks(files): total = 0 for src in files: print "Processing", src data = read_vm_file(src) for referrer, target, num_clicks in data: total += 1 return total
def create_categories(src, dest, hosts): print "Processing %s" % dest data = read_vm_file(src) with open(dest, 'w') as destf: writer = csv.writer(destf, delimiter="\t") for referrer, target, num_clicks in data: if is_member(referrer, hosts): writer.writerow([referrer, target, num_clicks])
def filter_targets(src, dest):
print "Processing %s" % src
data = read_vm_file(src)
with open(dest, 'w') as destf:
writer = csv.writer(destf, delimiter="\t")
for referrer, target, num_clicks in data:
if not should_skip_host(target):
writer.writerow([referrer, target, num_clicks])
def filter_referrers(src, dest): print "Processing %s" % src data = read_vm_file(src) with open(dest, 'w') as destf: writer = csv.writer(destf, delimiter="\t") for referrer, target, num_clicks in data: if not should_skip_host(referrer): writer.writerow([referrer, target, num_clicks])
def count_targets(files): targets = set() for src in files: print "Processing", src data = read_vm_file(src) for referrer, target, num_clicks in data: if target not in targets: targets.add(target) return len(targets)
def filter_news_junk(src, dest): print "Processing %s" % src data = read_vm_file(src) with open(dest, 'w') as destf: writer = csv.writer(destf, delimiter="\t") for referrer, target, num_clicks in data: target = normalize_url(target) if not should_skip_host(target): writer.writerow([referrer, target, num_clicks])
def show_top_targets(src):
print "Processing %s" % src
data = read_vm_file(src)
click_counts = {}
for referrer, target, num_clicks in data:
if target not in click_counts:
click_counts[target] = 0
click_counts[target] += num_clicks
return click_counts
def count_clicks_from_referrer(files, referrers):
counts = {}
for src in files:
print "Processing", src
data = read_vm_file(src)
for referrer, target, num_clicks in data:
if referrer in referrers:
if not referrer in counts:
counts[referrer] = 0
counts[referrer] += 1
return counts
def remove_unwanted(src, dest): print "Processing %s" % src data = read_vm_file(src) with open(dest, 'w') as destf: writer = csv.writer(destf, delimiter="\t") for referrer, target, num_clicks in data: try: r = normalize_url(referrer) t = normalize_url(target) if not should_skip_host(t): writer.writerow([r, t, num_clicks]) except: print "Couldn't normalize. Skipping." print referrer print target
def combine_files(files, destfile):
print "Processing %s" % destfile
clicks = {}
for f in files:
data = read_vm_file(f)
for referrer, target, num_clicks in data:
if referrer not in clicks:
clicks[referrer] = {}
if target not in clicks[referrer]:
clicks[referrer][target] = 0
clicks[referrer][target] += num_clicks
with open(destfile, 'w') as destf:
writer = csv.writer(destf, delimiter="\t")
for referrer in clicks:
for target in clicks[referrer]:
writer.writerow([referrer, target, clicks[referrer][target]])
def combine_files(files, destfile):
print "Processing %s" % destfile
clicks = {}
for f in files:
data = read_vm_file(f)
for referrer, target, num_clicks in data:
if referrer not in clicks:
clicks[referrer] = {}
if target not in clicks[referrer]:
clicks[referrer][target] = 0
clicks[referrer][target] += num_clicks
with open(destfile, 'w') as destf:
writer = csv.writer(destf, delimiter="\t")
for referrer in clicks:
for target in clicks[referrer]:
writer.writerow([referrer, target, clicks[referrer][target]])
def smooth_vm(files, dest):
print "Processing %s" % dest
vms = [read_vm_file(f) for f in files]
data = {}
for vm in vms:
for referrer, target, num_clicks in vm:
if referrer not in data:
data[referrer] = {}
if target not in data[referrer]:
data[referrer][target] = 0
data[referrer][target] += num_clicks
with open(dest, 'w') as destf:
writer = csv.writer(destf, delimiter="\t")
for referrer in data.keys():
for target in data[referrer].keys():
writer.writerow([referrer, target, data[referrer][target]])
def smooth_vm(files, dest):
print "Processing %s" % dest
vms = [read_vm_file(f) for f in files]
data = {}
for vm in vms:
for referrer, target, num_clicks in vm:
if referrer not in data:
data[referrer] = {}
if target not in data[referrer]:
data[referrer][target] = 0
data[referrer][target] += num_clicks
with open(dest, 'w') as destf:
writer = csv.writer(destf, delimiter="\t")
for referrer in data.keys():
for target in data[referrer].keys():
writer.writerow([referrer, target, data[referrer][target]])
def count_top_targets_per_file(src, dest):
print "Processing %s" % src
data = read_vm_file(src)
click_counts = {}
for referrer, target, num_clicks in data:
if target not in click_counts:
click_counts[target] = 0
click_counts[target] += num_clicks
total_count = float(numpy.sum(click_counts.values()))
sorted_counts = sorted(click_counts.items(), key=lambda tupl: tupl[1], reverse=True)
cum_count = 0
with open(dest, "w") as destf:
writer = csv.writer(destf, delimiter="\t")
for target, count in sorted_counts:
cum_count += count
writer.writerow([target, count, (total_count - cum_count) / total_count])
def index_vms(files):
print "Loading index."
vms = {}
for filepath in files:
data = read_vm_file(filepath)
for referrer, target, num_clicks in data:
if referrer not in vms:
vms[referrer] = {}
if target not in vms[referrer]:
vms[referrer][target] = 0
vms[referrer][target] += num_clicks
index = []
total = 0
for referrer in vms:
for target in vms[referrer]:
total += vms[referrer][target]
index.append((referrer, target, total))
return index
def index_vms(files):
print "Loading index."
vms = {}
for filepath in files:
data = read_vm_file(filepath)
for referrer, target, num_clicks in data:
if referrer not in vms:
vms[referrer] = {}
if target not in vms[referrer]:
vms[referrer][target] = 0
vms[referrer][target] += num_clicks
index = []
total = 0
for referrer in vms:
for target in vms[referrer]:
total += vms[referrer][target]
index.append((referrer, target, total))
return index
def count_top_targets_per_file(src, dest):
print "Processing %s" % src
data = read_vm_file(src)
click_counts = {}
for referrer, target, num_clicks in data:
if target not in click_counts:
click_counts[target] = 0
click_counts[target] += num_clicks
total_count = float(numpy.sum(click_counts.values()))
sorted_counts = sorted(click_counts.items(),
key=lambda tupl: tupl[1],
reverse=True)
cum_count = 0
with open(dest, 'w') as destf:
writer = csv.writer(destf, delimiter="\t")
for target, count in sorted_counts:
cum_count += count
writer.writerow(
[target, count, (total_count - cum_count) / total_count])
def change_domain_levels(src, dest, new_domain_level):
print "Processing", src
data = read_vm_file(src)
clicks = {}
for referrer, target, num_clicks in data:
if referrer is None or target is None or num_clicks == -1\
or domain_level(referrer) == 1 or domain_level(target) == 1:
continue
newr = change_domain_level(referrer, new_domain_level)
newt = change_domain_level(target, new_domain_level)
if newr not in clicks:
clicks[newr] = {}
if newt not in clicks[newr]:
clicks[newr][newt] = 0
clicks[newr][newt] += num_clicks
with open(dest, 'w') as destf:
writer = csv.writer(destf, delimiter="\t")
for referrer in clicks:
for target in clicks[referrer]:
writer.writerow([referrer, target, clicks[referrer][target]])
def sample_vm(src, dest, num_clicks_to_sample): return sample_vm_data(read_vm_file(src), dest, num_clicks_to_sample)
def sample_vm(src, dest, num_clicks_to_sample):
return sample_vm_data(read_vm_file(src), dest, num_clicks_to_sample)
